A Markov model for predicting performance on criterion-referenced tests is presented,. The model is expressed mathematically as a function of transition matrix, a current state vector, and a future state vector. The matrix is defined in terms of conditional probabilities, i.e., the probability of making a transition to a specific future performance state given data pertaining to the student's current performance state. Performance is expressed in terms of mastery, a theoretical construct that is defined in the paper. State vectors indicate either the probability of mastery or the degree of mastery. The current state vector can be computed from available observed criterion test scores. Three examples are included which indicate how transition matrices may be computed. An example is also provided which shows how the model can be used to predict future performance. Finally, a research application and a management application of the Markov model are mentioned. (Author)