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Even if represented in a way which is invariant to illumination conditions, a 3D object gives rise to an infinite number of 2D views, depending on its pose. It has been recently shown ([13]) that it is possible to synthesize a module that can recognize a specific 3D object from any viewpoint, by using a new technique of learning from examples, which are, in this case, a small set of 2D views of the object. In this paper we extend the technique, a) to deal with real objects (isolated paper clips) that suffer from noise and occlusions and b) to exploit negative examples during the learning phase. We also compare different versions of the multilayer networks corresponding to our technique among themselves and with a standard Nearest Neighbor classifier. The simplest version, which is a Radial Basis Functions network, performs less well than a Nearest Neighbor classifier. The more powerful versions, trained with positive and negative examples, perform significantly better. Our results, which may have interesting implications for computer vision despite the relative simplicity of the task studied, are especially interesting for understanding the process of object recognition in biological vision.