Cognitive radio systems require detection of different signals for communication. In this paper, an approach for multiclass signal classification based on second-order statistical features and multiclass Support Vector Machine (SVM) classifier is proposed. The proposed system is designed to recognize three different digital modulation schemes such as PAM, 32QAM and 64QAM. The signal classification is achieved by extracting the 2nd order cumulants of the real and imaginary part of the complex envelope and these second-order statistical features are given to multiclass SVM classifier for classification. The modulated signals are passed through an Additive White Gaussian Noise (AWGN) channel before feature extraction. The evaluation of the system is carried on using 400 generated signals. Experimental results show that the proposed method produces an accurate classification rate with 65-89% for 1024 samples.