Behavior recognition provides an interesting perspective for understandingthe different modes of a system and the influence of eachmode under varying conditions. In most of the systems, prior knowledgeof different expected behavior is available. Whereas, in an automotivedomain, a fleet of vehicle with many external factors influencingeach vehicle and an asynchronous performance of each vehicleon road, creates the complexity on analyzing and predicting the exacttime segments of vehicles in a fleet exhibiting similar behavior. Thisthesis focuses on recognizing time segments of vehicles that exhibitsimilar behavior based on supervised and unsupervised approaches.In supervised approach, classifiers are trained to predict two distinctiveoperations(highway and in-city). In unsupervised approach, featurespace is explored for identification of consistent features and existenceof other operations. An unsupervised approach to recognizepeer cluster groups is combined with supervised classification resultsto achieve lower computational complexity.