Labor accidents cause several misfortunes, such as inconvenience to the injured ones, loss of laborproductivity, and public spending on aid and accident compensation. This work aims to search and characterize groupsof labor accidents, granting interpretability to the obtained results, to extract information that can be relevant to publicmanagers. The method proposed in this work consists of the following steps: data pre-processing; the applicationof two hierarchical clustering algorithms, HDBSCAN * and COBWEB; the evaluation of results using the SimplifiedSilhouette. The research demonstrated the susceptibility of male workers, focused on ages between 18 and 34 years old,with labor accidents that caused injuries on the fingers, by handling machines and equipment or manual tools, followedby those activities such as fishing. Considering clusters majorly composed by female victims, those related to work incellulose, paper, and related products stand out. Moreover, fingers are the most affected part, featured for incidentscaused by the handling of chemical, biological, or hand tools.