One of the intelligent aspects of human beings in pattern recognition is that man identifies an object in real world using Marked Characteristic Principle (MCP). This paper proposes a humanoid recognition method for radar emitter signals. The main points of the method include feature ordering and an improved one-versus-rest multiclass classification support vector machines. According to MCP, an approach for computing marked characteristic coefficients is presented to obtain the most marked feature of every radar emitter signal. Subsequently, a support vector network is designed using the improved one-versus-rest combination approach of several binary support vector machines. Experimental results show that the introduced method has faster recognition speed and better classification capability than conventional recognition approaches.