A strategy combining advantages of view-based and model-based object recognition approaches has been developed. Textured 3D models are used to produce local appearances of object key-points. Correspondences between 3D points and local appearances are also established during this learning stage. The recognition consists first in matching these local appearances and those extracted from the image. Robust 3D pose estimation is then carried out, discarding spurious correspondences. This algorithm is computationally very efficient. It can analyze images including several objects and can handle partial object occultations as well as important changes of brightness and contrast.