Systems with a single sensor (radar or infrared image sensor) have their limitations in target recognition and tracking. A system with multi-sensors can fuse data from different sensors to overcome the limitations in the system with a single sensor, it can make use of the complement and redundancy of data from different sensors to improve the precision of target recognition and tracking and the robustness and reliability. In our system for target recognition and tracking, radar and infrared image sensors are used. As a radar sensor in our system can provide with the information of the distance and direction of the target (not the image of the target), data fusion is implemented only at characteristic level and at decision level. For data fusion at characteristic level, characteristics of a target obtained from radar can be used in the IR Image-based subsystem to improve the ability of object recognition, and vice versa. The process of target recognition based on IR image analysis is composed of image enhancement, image segmentation and recognition of segmented objects. For image enhancement, median-filter, histogram equalization, wavelet transformation and canny-operator are used. A median-filter is used for the elimination of punctate noises in an IR image. The grayness of a pixel in an IR image is determined by the grayness of its neighbors. Histogram equalization is used for the improvement of the contrast of an IR image. Multi-scale pyramidal wavelet transformation is used to delete unexpected edges and improve the continuity of edges in an IR image. Canny operator based on Gauss-function is used for edge detection, an IR image is transformed into a binary image based on the adaptive threshold. According to area (number of pixels) of segmented objects, the recognition of segmented objects is divided into two classes: recognition of dot targets and area targets. Rule-based reasoning is used to deal with the recognition of dot targets; a classifier based on neural network is used to deal with the recognition of area targets. The models for target recognition are extracted by data mining. The rules for the recognition of dot targets are extracted by decision trees. A neural network for the recognition of area targets is constructed by multi-layer preceptron and trained by training examples. The following characteristics of target are used as inputs of the neural classifier: •distance of the target obtained from the radar-based subsystem. •area of the target in the IR image, the variation of areas of the target in the consecutive two IR images. •the mean grayness of pixels of the target. •the variation of centers of the target in the consecutive two IR images. •the topological shape of the target (seven moment invariants Ψ1,Ψ2,...,Ψ7, the number of forks in the frame extracted.) •the direction of the target motion predicated by radar and the relation of angles among the axes of the missile, Radar and IR image sensor. After data fusion at characteristic level, a true target is recognized by the radar-based subsystem and the IR image-based subsystem. Based on these two decisions, data fusion at decision level is to make a final decision of target tracking. a factor "decision certainty" is introduced to realize data fusion at decision level, which represents the relative certainty of decisions of target tracking.