Current research on target detection and recognition from synthetic aperture radar(SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition(ATR), especially for the detection and recognition of vehicles, an algorithm based on kernel fisher discriminant analysis(KFDA) is proposed.First, in order to make a better description of the difference between the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area,we propose an improved KFDA-IMED(image Euclidean distance)combined with a support vector machine(SVM) to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recognition rate. Current research on target detection and recognition from synthetic aperture radar (SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition (ATR), especially for the detection and recognition of vehicles, an algorithm based on kernel fisher discriminant analysis (KFDA) is proposed. First, in order to make a better description of the difference between the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED (image Euclidean distance) combined with a support vector machine (SVM) to r On the detection task, our proposed method availability of only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recognition rate.