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Discrimination of Ventricular Tachycardia (VT) from Supra-Ventricular Tachycardia (SVT) remains a major challenge for appropriate therapy delivery in Implantable Cardioverter Defibrillators (ICDs), especially in single chamber devices. We propose here a new discrimination algorithm that analyzes, with a machine learning approach, the morphology of a two-dimensional representation of both a far-field and a near-field ventricular sensing channel. Features extracted from this representation allow comparisons between curves. Thus, arrhythmia discrimination is performed by comparing an arrhythmia curve to a reference curve. A statistical classifier was trained on a private database and tested on the standard Ann Arbor Electrogram Libraries. Our discrimination algorithm demonstrated high sensitivity and specificity for VT/SVT discrimination. The requirements of this algorithm make it appropriate for implementation in the simplest ICD system.