Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person-fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person-fit tests take the correlation between ability and speed into account, as well as the correlation between item characteristics. They are posited as Bayesian significance tests, which have the advantage that the extremeness of a test statistic value is quantified by a posterior probability. The person-fit tests can be computed as by-products of a Markov chain Monte Carlo algorithm. Simulation studies were conducted in order to evaluate their performance. For all person-fit tests, the simulation studies showed good detection rates in identifying aberrant patterns. A real data example is given to illustrate the person-fit statistics for the evaluation of the joint model.