Neural networks are a group of computer-based pattern recognition technologies that have been applied to problems in clinical diagnosis. This review focuses on one member of the group of neural networks, the backpropagation network. The steps in creating a backpropagation network are (1) collecting adequate training facts, (2) choosing the specific network structure, (3) training the network, and (4) cross-validating the trained network. The first published applications of backpropagation networks to problems in pathology and laboratory medicine have appeared recently. These applications are in the areas of image analysis and interpretation of laboratory results, and they demonstrate the feasibility of the approach.