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This paper proposes a real-time vehicle management system using a vehicle tracking and a car plate number identification technique. The system uses two cameras: one for tracking vehicles and another for capturing LP (license plate). We track the vehicles by applying the CONDENSATION algorithm over the vehicle's movement image captured from the first camera. To render the CONDENSATION algorithm more effective, we build a discrete vehicle shape model by training vehicle patterns with a SOM (self organizing map), which makes the system suitable for real-time application. Next, we take the probabilistic dynamic model such as HMM (hidden Markov model) to reflect the temporal change in shape of various vehicles. As a vehicle reaches the designated target line, a signal is sent to the second camera for capturing the vehicle's front side. The captured image is transferred to an LPR (vehicle LP recognition system) which recognizes the vehicle's category and LP. LPR system detects the vehicle LP using the only the vertical edge of the captured vehicle image, and effectively accomplishes the character segmentation of the LP region using the geometric transformation without respect to the position and angle of the CCD camera. The segmented characters are recognized using the SVM (support vector machine). By combining these two techniques, we construct a real-time automatic vehicle management system that can be used to control vehicle parking and searching for specific vehicles.