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Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non\u2010animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens\u2122 assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave\u2010one\u2010out cross\u2010validation. A one\u2010tiered strategy modeled all three categories of response together while a two\u2010tiered strategy modeled sensitizer\/non\u2010sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two\u2010tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one\u2010tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non\u2010animal methods may provide valuable information for assessing skin sensitization potency. 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