This article relates to a study that deals with the linguistic analysis of proform in French Sign Language (LSF). To our knowledge, these structures remain poorly defined and are almost never taken into account in computer modelling, which is the longer-term goal of our studies that aims to design Sign Languages (SL) automatic processing. Given their rather frequent use in SL discourses, it seems necessary to us to study the different linguistic functions that they can take and the most frequent observable forms associated with these functions. This requires us to study which body articulators are involved (manual and non-manual) and how they are organised within spatiotemporal structures. In this article, we present the results of an exploratory study concerning one of these functions, the use of proforms to introduce an entity into the discourse. We have carried out a corpus analysis which revealed particular structures associated with this function and not described so far. We have modelled them using a formalism that takes into account the multilinear, spatiotemporal and iconic structure of SL, and which can be used in automatic processing.For us, a "proform" is a conventional handshape, generally motivated and iconic, representing an entity that has been already mentioned in the discourse or not. The proform takes its full meaning only in context. We use the term proform without a priori regarding the level of language (morphology or syntax) or linguistic functionalities. A number of linguistic functions have been mentioned in the literature, coming with a variation in the terminology (“classifier”, “polymorphemic predicate”, productive sign”, etc). We have tried to identify the different functions of the proforms mentioned in the literature, which could be:classifying function: An ambivalent function, since "classifying" can mean both the semantic class or the classes of forms, depending on the theory.adjectival function: These are mainly descriptors or size and/or shape specifiers.anaphoric function: In the case where the proform is used as an entity already mentioned in the discourse, often in tandem with a relevant spatialisation.spatio-locative function: Often used in the case of classifier constructions.presentative function: Presented as the case of nominal classifiers that introduce an entity into the discourse.In our study, our aim was to examine in detail this last function, which is how the proforms are performed when introducing a new entity in a discourse. We wanted to determine which forms correspond to this function. For this, we adopted a corpus-based methodology. The main corpus that has been used is the MOCAP1 corpus, a corpus of LSF created to conduct multidisciplinary studies. One of the tasks of this corpus was to describe 25 different photographs. These descriptions include many uses of proforms with different linguistic functions, including the one we are interested in. We then proceeded to annotate the corpus by identifying the temporal segments corresponding to the activity of the manual articulators (lexical signs, pointing, illustrative structures including the use proforms, etc) and non-manual ones (head, eyebrows, closing of the eyes, direction of gaze, cheeks, lips).For this study, we took into account the annotations carried out on 5 of the images for the 8 signers (40 videos) and carried out a first step of essentially qualitative analysis. These images were chosen for the greatest richness they generated in the descriptions. We do not yet have at this stage elements of a statistical nature to give. Statistical analysis would require extending the annotation to other videos in the corpus and other types of discourses in order to have enough occurrences for each observed phenomenon. At this stage, our aim is mainly to identify and describe structures that seem to be frequently produced by the signers.In order to describe the recurrent patterns observed, we used a formal representation called AZEE which takes into account the specificities of signed languages. AZEE was chosen among other systems because it allows to represent the activity of the different manual and non-manual articulators with the necessary and sufficient constraints for their realisation in any spatial and temporal context. This representation also aims to be implementable and thus usable for sign language processing.We focused particularly on three structures:First, the introduction of an entity with a “buoy” (perseveration of a handshape coming from a gestural unit previously signed). We observed that the entities that were introduced with a buoy are performed with a proform maintained by the non-dominant hand, while lexical elements (with the dominant hand) specify this proform. The structure ends with a blink.Second, the introduction of an entity with a sequential organisation: the succession of a proform and lexical elements that specify the proform. In the same way, the structure ends with a blink.These two structures can be combined.Finally, proforms without a manual referent. These structures contain a proform, but rather than a manual element that specifies it, there is some mouthing or mouth gestures. As with the two previous structures, this one ends with a blink.Using proforms to introduce a new entity is not a discovery. But finding recurrent structures could be a good start with the prospect of formalisation for automatic processing of signed languages.Nevertheless, before any modelling, questions remain unanswered. On the one hand, we started from the premise that buoy structures could be considered as the manifestation of an anaphoric relationship between the proform and the specifier, but this can still be discussed. On the other hand, sequential structures offer less doubt about an analysis in favor of an anaphoric relationship. For proforms with a non-manual and simultaneous specifier, this also raises questions.If we end this article with more questions than precise answers, we think that finding several structures implementing various articulators in a systematic way could eventually allow automatic identification and/or generation of these structures. On the other hand, if identification of a referent could be done more easily, it could help the automatic processing of the reference, if we admit that there is reference (especially for the case of tags).Obviously, this is the first step of an observation that will have to be refined and validated on a larger corpus. But if a generalisation of these structures is possible, then we will be able to define patterns usable in SL processing.