Hand washing is the simplest and most effective gesture, when correctly performed, for the prevention of many infections. For this reason, the World Health Organization (WHO) has defined a washing procedure that guarantees effective and safe cleaning. This organization recommends that States promote this activity and monitor it continuously. Based on this fact, this article presents a work oriented to study the feasibility of identifying the moments in which a person carried out a hand washing, determining its beginning and duration, as well as if these washings were compliant with the WHO guidelines. The identification of washing moments is made through the analysis, by means of Machine Learning techniques, of the data that can be collected from the inertial sensors of the smartwatch the person is wearing. This study was carried out with the participation of 15 volunteers. Data was not only collected in controlled settings but, also, more than 600 hours of sensor measurements come from free-live conditions. The results of the study showed that it is feasible to build a solid solution based on the use of low cost wearables for the identification of washing moments. The solution is very effective (with F1 over 95%) with user-dependent models. Also, with user-independent models, the identification of WHO washings is also very effective (with F1 above 85%), but more limited in the detection of free washings (F1 around 55%).