The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive test specifications. These specifications identify the examinees’ attribute patterns which are used as output for the neural network. The purpose of the statistical pattern recognition analysis is to estimate the probability that an examinee possess specific attribute combinations based on their observed item response patterns. Two examples using student response data from a sample of algebra items on the SAT illustrate our pattern recognition approach.