This paper studies the problem of radar target recognition based on radar cross section(RCS)observation sequence.First,the authors compute the discrete wavelet transform of RCS observation sequence and extract a valid statistical feature vector containing five components.These five components represent five different features of the radar target.Second,the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target.By set-valued identification method,the authors can estimate the system parameter,based on which the recognition criteria is given.In order to illustrate the efficiency of the proposed recognition method,extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed.The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method. This paper studies the problem of radar target recognition based on radar cross section (RCS) observation sequence. First, the authors compute the discrete wavelet transform of RCS observation sequence and extract a valid statistical feature vector containing five components. These five components represent five different features of the radar target. Second, the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target. By the set-valued identification method, the authors can estimate the system parameter, based on which the recognition criteria is given. In order to illustrate the efficiency of the proposed recognition method, extensive simulations are given the ultimate goal that the true target is a cone frustum and the RCS of the false target is normally distributed. Results show that the set- valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.