Correct detection and classification of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is of pivotal importance for an automatic external defibrillator and patient monitoring. In this paper, a VF/VT classification algorithm using a machine learning method, a support vector machine, is proposed. A total of 14 metrics were extracted from a specific window length of the electrocardiogram (ECG). A genetic algorithm was then used to select the optimal variable combinations. Three annotated public domain ECG databases (the American Heart Association Database, the Creighton University Ventricular Tachyarrhythmia Database, and the MIT-BIH Malignant Ventricular Arrhythmia Database) were used as training, test, and validation datasets. Different window sizes, varying from 1 to 10 s were tested. An accuracy (Ac) of 98.1%, sensitivity (Se) of 98.4%, and specificity (Sp) of 98.0% were obtained on the in-sample training data with 5 s-window size and two selected metrics. On the out-of-sample validation data, an Ac of 96.3% ± 3.4%, Se of 96.2% ± 2.7%, and Sp of 96.2% ± 4.6% were obtained by fivefold cross validation. The results surpass those of current reported methods.