Electromyographs are used for electromyography signal extraction from neurologically activated muscle cells. These signals are investigated to extract discriminating patterns to be categorized in the classification stage of myoelectric control systems (MCSs) designed for various applications. Feature extraction is a fundamental step in EMG signal processing which affects the overall performance of MCSs. To improve classification accuracy of MCSs, this paper proposes a novel approach for feature extraction from time-frequency images of EMG signals using local binary patterns and gray level co-occurrence matrices. In contrast to time alone and frequency alone approaches, by textural analysis of EMG signal spectrogram, time-frequency patterns of these signals are revealed, simultaneously. Furthermore, LBP and GLCM expose relational properties of time-frequency patterns which areexploited as the main features for classification. EMG physical action dataset is utilized in this study to evaluate the proposed method. In the classification stage, support vector machine classifiers are used in two segmented and holistic modes. The best classification accuracy of 98.75% is obtained by segmented approach which is superior to the results provided by state of the art methods.