This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.