Due to its great practical importance, Intelligent Transportation System has been an active research area in recent years. In this paper, we present a framework that incorporates various aspects of an intelligent transportation system with its ultimate goal being vehicle classification. Given a traffic video sequence, the proposed system first proceeds to segment individual vehicles. Then the extracted vehicle objects are normalized so that all vehicles are aligned along the same direction and measured at the same scale. Following the preprocessing step, two classification algorithms - Eigenvehicle and PCA-SVM, are proposed and implemented to classify vehicle objects into trucks, passenger cars, vans, and pick-ups. These two methods exploit the distinguishing power of Principal Component Analysis (PCA) at different granularities with different learning mechanisms. Experiments are conducted to compare these two methods and the results demonstrate the effectiveness of the proposed framework.