The effect of person misfit to an item response theory model on a mastery/nonmastery decision was investigated. Furthermore, it was investigated whether the classification precision can be improved by identifying misfitting respondents using person-fit statistics. A simulation study was conducted to investigate the probability of a correct classification using different cutoff points, estimation methods, person-fit statistics, model violations, test lengths, and sample sizes. The effect of the presence of misfitting item score patterns on the item parameter estimates was also taken into account. Results showed that the effect of the presence of misfitting item score patterns on the classification of nonaberrant simulees was generally small (i.e., the classification precision for these simulees did not go down). Furthermore, for simulees classified as nonaberrant using a person-fit statistic, the classification decisions were comparable with the classification decisions for actual nonaberrant simulees. These results were comparable across different person-fit statistics and estimation methods.