Based on the large-scale scene synthetic aperture radar (SAR) image series, a combined recognition and tracking method for various slowly-moving vehicle targets is presented. The method introduces an idea named recognition first and tracking later. A recognition algorithm combining local multi-resolution analysis for image target and multiple kernel classifier is studied, which realizes the high-speed feature extraction and accurate classification of various image targets. According to the corresponding relationship of targets in the adjacent frames, the targets′ motion parameters are estimated utilizing the unscented Kalman filter. Simultaneously, the real-time location and target type can be obtained via continuous correction using the measured values; as a result, high performance tracking of various slowly-moving ground targets in complicated background is realized. Large-scale scene SAR series images are constructed and several simulation tests are performed, demonstrating that the method has good convergence, excellent real-time performance, and high tracking precision.