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A new islanding detection method based on image classification with support vector machine is proposed in this study. Histogram of oriented gradient features is extracted from the image for classifying non-islanding and islanding events. In the proposed technique, the time-series signal acquired from the point of common coupling is first converted into an image. Histogram of oriented gradient features is extracted from the image, which is used as an input feature vector for training and testing multiple support vector machine classifiers. Parameters such as voltage, rate of change of voltage, and rate of change of negative sequence voltage are used. Furthermore, a feature for early islanding detection is also presented to detect an islanding event even before it has occurred. The detection accuracy of the proposed method is tested with different kernels. The performance of all the classifiers is tested with 5-fold cross-validation. The classification results show that islanding detection with image classification based on the histogram of oriented gradient feature and multiple support vector machine classifiers can achieve excellent results. [ABSTRACT FROM AUTHOR]