This paper proposes an experimental evaluation of state of the art 2D/3D, view-based indexing methods. The principle of 2D/3D indexing methods consists of describing 3D models by means of a set of 2D shape descriptors, associated with a set of corresponding 2D views (under the assumption of a given projection model). Several experiments were conduced in order to examine the influence of the number of views and of the associated viewing angle selection strategies on the retrieval results. Experiments concern both 3D model retrieval and image recognition from a single view. Three 2D shape descriptors were tested in order to determine which of them is the most suited for such approaches. Results obtained show promising performances, with recognition rates from a single view higher than 80%, which opens interesting perspectives in terms of semantic metadata extraction from still images/videos.