This paper presents a statistical methodology that uses both acceleration and angular velocity signals to detect critical safety events for Powered Two Wheelers (PTW). The problem of recognition of critical events has been performed towards two steps: (1) the feature extraction step, where the multidimensional time trajectories of accelerometer/gyroscope data were modeled and segmented by using a specific mixture model with quadratic logistic proportions; (2) the classification step, which consists in using the k-nearest neighbor (k-NN) algorithm in order to assign each trajectory characterized by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. The results show the ability of the proposed methodology to detect critical safety events for Powered Two Wheelers.