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However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62\u2013100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86\u201390%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. 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