This paper introduces the use of ECG signals from multiple leads to improve the accuracy of ECG signal classification with Artificial Neural Networks (ANN). The current methods commonly proposed rely on advanced signal processing or statistical analysis of the main lead II (MLII) in order to extract features that serve as a description of the signal. MLII, while being the most easily obtained ECG signal, does not contain a complete description of the electrical activity of the heart. Therefore, we propose to include a precordial lead, V1, from the Standard 12-Lead ECG system, to give the neural network a 2D view of the electrical patterns that arise during heart activation. This method was shown to be 99.5% accurate.