In this paper we describe the various steps of a method using local descriptors in order to classify freehand sketches, which are drawn in an unconstrained manner and recorded online. Our final objective is to simultaneously allow classification, comparison or clustering of those elements according to their Visuo spatial properties, their realization strategies, their structural or temporal parameters, or any combination of two or more of those parameters. The context and the motivation of this work-in-progress are described. First, we'll explain the detection step: we decided to use a mixed segmentation process. Indeed recognition methods based on local descriptor are not fully adapted to our types of shapes and to the one-line context. Later, we will show how we plan to give a global perception of the shape to our local descriptor by using segmentation results. Then we will introduce the local descriptor and how it is computing. Next we'll explain how we calculate the dissimilarity between two descriptors. Finally, we'll describe the approach we have chosen to determine the dissimilarity between shapes represented by sets of local descriptors.