Three-dimensional (3-D) object recognition from digitized intensity images is a central problem in providing computers with humanlike perception capabilities. We present a neural system that performs learning and classification of 3-D planar-faced objects. These objects are described through a set of line descriptors that provide a type of invariance to scaling and allow a reduction in the number of views needed to train the network. Kohonen networks have been used to allow a humanlike classification of the object views. Each network is capable of discriminating between several distinct objects and can be combined in a modular way with similar networks to build large multiobject classifiers.