In this article, we propose using the Bayes factors (BF) to evaluate person fit in item response theory models under the framework of Bayesian evaluation of an informative diagnostic hypothesis. We first discuss the theoretical foundation for this application and how to analyze person fit using BF. To demonstrate the feasibility of this approach, we further use it to evaluate person fit in simulated and empirical data, and compare the results with those of H[superscript T] and the infit and outfit statistics. We found that overall BF performed as well as H[superscript T] statistics and better than the infit and outfit statistics when detecting aberrant responses. Given the BF flexibility in handling data set with a small number of examinees, we suggest that BF can be used as person fit statistics, especially in computerized adaptive tests.