Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the termination criterion, namely the algorithm that decides whether to classify the examinee and end the test or to continue and administer another item. This paper applies a newly suggested termination criterion, the generalized likelihood ratio (GLR), to CCT. It also explores the role of the indifference region in the specification of likelihood-ratio based termination criteria, comparing the GLR to the sequential probability ratio test. Results from simulation studies suggest that the GLR is always at least as efficient as existing methods. (Contains 3 figures and 4 tables.)