ABSTRACT
Artificial neural networks (ANNs) are applied to the problem of classifying obsidian rock samples taken from the West New Britain region of Papua New Guinea. Multilyer perceptrons, self-organizing maps and learning vector quantization are found to be the most appropriate models for this task. A somewhat surprising result is that ANNs are able to yield good results (at least comparable with a human expert) with very few training exemplars.
