This study focused on the robustness of unidimensional item response theory (UIRT) models in computerized classification testing against violation of the unidimensionality assumption. The study addressed whether UIRT models remain acceptable under various testing conditions and dimensionality strengths. Monte Carlo simulation techniques were used to assess the robustness of the UIRT models by comparing the false positive classification rates, false negative classification rates, and numbers of items administered for classification or mastery under the different conditions. In this study, both unidimensional three-parameter and one-parameter models were found robust with the sequential probability ratio testing procedure in computerized classification testing. The UIRT model chosen, the strength of the dimensionality, and testing conditions had an impact in terms of classification accuracy and test efficiency. (Contains 2 figures, 4 tables, and 11 references.) (Author/SLD)