Abstract: An adaptive distance calculating neural network classifier chip accepts high dimensionality input pattern vectors with up to 256 5-bit elements per vector and compares the input vector with up to 1024 prototype vectors stored on-chip by calculating the distance between the input vector and each of the prototype vectors. The classifier further provides for identifying up to 64 classes to which the prototype vectors belong. If the distance between input and prototype vector is less than a programmable threshold distance, the prototype fires and the class to which it belongs is identified. If prototype vectors belonging to more than one class fire, a probabilistic model based on Parzen windows may be invoked to resolve the classification by providing the relative probabilities of various class membership. The classifier chip is trainable by supplying appropriate training vectors and associated class membership.