A connectionist network has been presented here for simultaneous perception of multiple categories. These categories provide an adequate explanation of the input features originating from multiple classes. The network optimises an appropriately defined error function for making the inference. A supervised learning algorithm is presented for learning the association between the features and each individual category. © 1992 Institute of Electrical and Electronics Engineers Inc. All rights reserved.