Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3 vs. 5), and items (25 vs. 50) as well as different attribute correlations (0.50 vs. 0.80) and marginal attribute difficulties (equal vs. different). We investigated misspecifications of interaction effect parameters under correct Q-matrix specification and two types of Q-matrix misspecification. While the misspecification of interaction effects had little impact on classification accuracy, invalid Q-matrix specifications led to notably decreased classification accuracy. Two proposed item-fit indexes were more strongly sensitive to overspecification of Q-matrix entries for items than to underspecification. Information-based fit indexes AIC and BIC were sensitive to both over- and underspecification.