Osteologists commonly assess the sex of skeletal remains found in forensic and archaeological contexts based on ordinal scores of subjectively assessed sexually dimorphic traits. Using known-sex samples, logistic regression (LR) discriminant functions have been recently developed, which allow sex probabilities to be determined. A limitation of LR is that it emphasizes main effects and not interactions. Chi-square automatic interaction detection (CHAID) is an alternative classification strategy that emphasizes the information in variable interactions and uses decision trees to maximize the probability of correct sex determinations. We used CHAID to analyze the predictive value of the 31 possible combinations of five sexually dimorphic skull traits that Walker used previously to develop logistic regression sex determination equations. The samples consisted of 304 individuals of known sex of English, African American, and European American origin. Based on practical considerations, selection criteria for the best sex predictive trait combinations (SPTCs) were set at accuracies for both sexes of 75% or greater and sex biases lower than 5%. Although several of the trees meeting these criteria were produced for the English and European American samples, none met them for the African American sample. In the series of out-of-sample tests we performed, the trees from the English and combined sample of all groups predicted best. Am J Phys Anthropol, 2009. © 2009 Wiley-Liss, Inc.