The purpose of this study is to describe how the attribute hierarchy method (AHM) can be used to evaluate differential group performance at the cognitive attribute level. The AHM 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 on each attribute for each examinee thereby providing specific information about the examinee's attribute-mastery level. These probabilities can also be compared across groups. We describe a four-step procedure for estimating and interpreting group differences using the AHM. We also provide an example using student response data from a sample of algebra items on the SAT to illustrate our pattern recognition approach for studying group differences.