In last years vehicular network safety and security are attracting interest from both industries and researchers. In this paper, starting from a set of features gathered from the in-vehicle CAN bus, we show how machine learning algorithms can be useful to discriminate between the car owner and impostors. Furthermore, we assess several machine learning classifiers ability to predict instances not evaluated in the training set.