Abstract Neural networks were developed to study and mimic the functioning of the human brain. Humans are good at pattern recognition; the question is how good neural networks are at it, particularly with problems of forensic science interest. Simulation experiments with a type of neural network known as a Hopfield net indicate that it may have value for the storage of toolmark patterns (including bullet striation patterns) and for the subsequent retrieval of the matching pattern using another mark by the same tool for input. Another type of neural network, the back-propagation network (BPN), is useful for applications similar to those for which standard statistical methods of pattern classification can be used. This would be 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 currently necessary to try both to determine which would be best for any given situation.