This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis

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... ) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%.