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The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three <jats:italic>in chemico<\/jats:italic> or <jats:italic>in vitro<\/jats:italic> assays \u2013 the direct peptide reactivity assay (DPRA), human cell line activation test (h\u2010CLAT) and KeratinoSens\u2122 assay \u2013 six physicochemical properties and an <jats:italic>in silico<\/jats:italic> read\u2010across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h\u2010CLAT and read\u2010across; (2) DPRA, h\u2010CLAT, read\u2010across and KeratinoSens; or (3) DPRA, h\u2010CLAT, read\u2010across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63\u201379%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. 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