The modelling and measurement of expertise is a relatively new research area in artificial intelligence and cognitive science. Many domains do not have a formal method for evaluating expertise. When formal methods exist, they are frequently inefficient. Using an extension to the IAM program, a pattern recognition and acquisition method for evaluating the level of expertise for the domain of chess is developed. Chess players, as well as experts in other domains, use cognitive chunks of perceptual patterns to gain a cognitive economy that enables them to evaluate complex domain situations faster and more accurately than novices. The IAM program acquires a representative collection of the perceptual patterns demonstrated by a domain expert and uses those patterns to analyse skill level. A longitudinal study of a developing player and a comparison of the developing player to an established expert demonstrates the utility of the developed method for evaluating expertise.