In this thesis work a real case problem concerning heavy duty vehicles’ usage patterns identification is addressed. Even if in the literature there are several cars usage patterns identification, the same kind of analysis is less frequently carried out on industrial, construction or off-roads vehicles. However, thanks to the wide spread of IoT devices and the firmly established cars connected mobility, the heavy-duty in-vehicle connectivity is growing in importance. To this purpose, multivariate analysis of multiple CAN signals techniques based on clustering and patterns discovery from time series data is presented. At first, ultra-fine, asynchronous and heterogeneous signals have been analysed: the relevant parameters to be monitored have been identified, the most appropriate level of aggregation of data has been suggested and series characterized by different sampling rates have been properly combined.