Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-estab- lished, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate ap- proach that can accurately model spatial complexity as well as the interactions between neuroanatomical fea- tures. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) clas- si er to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genet- ics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential bene ts of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identi ed speci c cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the De- strieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).