An algorithm for supervised learning the segmentation of partially occluded objects is presented. It is based on the classification of object windows, which are small compared to the object size, but large enough to evaluate structural object features as well as color. From the input windows, features are extracted by local principal component analysis and subsequently classified by a neural network of the local linear map type. The performance is checked on images of objects with partial occlusion, which were artificially generated from the Columbia Object Image Library.