The discrimination between falls and blows is an important task in forensic anthropology and pathology. This research aimed to test a discrimination method between falls and blows. This method was created from the quotation of 549 types of fractures for 57 bones and 12 anatomical regions. Different models were tested according to the sensibility of random forest parameters and their effects on model accuracies. The best model was based on binary coding of 12 anatomical regions or 28 bones with or without baseline (age and sex). We tested this new method in the distinction between falls and blows on post-mortem computerized tomography scans (PMCT). The sample was composed of 47 subjects with 36 falls and 11 blows, whose aetiologia was based on forensic reports. Of the 47 bodies, 35 were complete, and 12 presented missing bones; 39 were estimated to be falls and 8 to be blows. Of the 12 individuals with missing bones, 11 had a reasonable estimation of the etiology of fractures, i.e., 91.7%. Methods showed excellent etiology estimation for fall cases (97.2%) but misclassified 36.4 % of blow cases. Our method misclassified 5 subjects (10.6%), more precisely, 4 blows and 1 fall. Overall, the reliability of the estimation of the etiology is substantial, with a Cohen’s k-values of 0.67. The method could be used in distinguishing between blows and falls and is also suitable for fragmented or missing bones. To ensure an easy and fast use of this method, we have developed a freely available online automated tool. Résumé: This research aims to test a method of discrimination between falls and blows. This method was created from the rating of 549 types of fractures, 57 bones and 12 anatomical regions. Different models were tested depending on the sensitivity of random forest parameters and their effects on model accuracy. The best model is based on binary coding of 12 anatomical regions or 28 bones with or without reference data (age and sex). We tested this new method of distinguishing between falls and blows on post-mortem CT scans (PMCT). The sample consists of 47 subjects, 36 falls and 11 blows, the etiology of which was based on medico-legal reports. Of the 47 bodies, 35 were complete, and 12 had missing bones; 39 were judged to be falls and 8 to be hits. Of the 12 individuals with missing bones, 11 had a good estimate, or 91.7%. Our method misclassified 5 subjects (10.6%) more precisely, 4 hits and 1 fall. The method showed excellent etiology estimation for fall cases (97.2%) but misclassified 36.4% of blow cases. The reliability of the etiology estimate is substantial with a Cohen’s kappa of 0.67. The method could be used to distinguish blows from falls and would also be suitable in the case of fragmented or missing bones. To ensure easy and quick use of this method, we have developed an automated tool, free and accessible online.