This paper describes and evaluates the use of measurement decision theory (MDT) to classify examinees based on their item response patterns. The model has a simple framework that starts with the conditional probabilities of examinees in each category or mastery state responding correctly to each item. The presented evaluation investigates: (1) the classification accuracy of tests scored using decision theory; (2) the effectiveness of different sequential testing procedures; and (3) the number of items needed to make a classification. A large percentage of examinees can be classified accurately with very few items using decision theory. A Java Applet for self instruction and software for generating, calibrating and scoring MDT data are provided. Accessed 13,741 times on https://pareonline.net from April 11, 2009 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.