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Automatic digital modulation recognition in intelligent communication systems is one of the most important issues in software radio and cognitive radio. In this paper a new method will be presented for automatic digital modulation classification in presence of additive white Gaussian noise (AWGN). In this method a set of three different types of features is extracted to be employed in recognition process. Classification is based on support vector machine (SVM) as a powerful method for pattern recognition, and particle swarm optimization (PSO) to configure kernel parameters. Computer simulations of 16 different types of digitally modulated signals corrupted by AWGN are carried out to measure the performance of the method. Employing multiple SVMs in a hierarchical structure as inter-class and intra-class classifiers and also our proposed method for feature selection based on features impact on severance, presents good results in simulations. The results show that with infinite SNR, accuracy tends to 99.9%. Also this method shows eligible robustness in presence of noise as we can see in experiments conducted using low SNR data.