This paper describes the application of a back-propagation network to handwritten digit recognition. The authors demonstrate how a network architecture, constrained by geometric knowledge, can achieve high accuracy in classifying digits without extensive preprocessing. The network, trained on a real-world dataset of handwritten zip codes, achieves a 1% error rate with a 9% rejection rate, showing promising results in this domain. The paper also highlights the network's efficient learning process and its potential for real-time implementation on commercial digital signal processing hardware.