Humans are good at pattern recognition; the question is how good neural networks are at it. Simulation experiments with a neural network known as the Hopfield net indicate that it may be valuable in the storage of toolmark patterns, including bullet striation patterns, and in the subsequent retrieval of the matching pattern using another mark by the same tool for input. The back propagation network (BPN), another type of neural network, is useful in applications similar to those for which standard statistical methods of pattern classification can be used. This network is an appropriate approach to the matching of general component patterns, such as gas chromatograms of gasoline, or pyrolysis patterns from materials of forensic science interest, such as paint. The BPN may provide better results than statistical methods, but it is necessary to try both the Hopfield net and the BPN to determine which is best for a given situation. Both networks generally require long learning times, which makes it prudent to try competing statistical methods initially to see if they provide adequate discrimination. Technical descriptions of both networks, including mathematical equations, are provided. 6 references, 3 tables, and 6 figures (Author abstract modified)