An electrocardiogram (ECG) is a bioelectrical signal which records the heart’s electrical activity versus time. It is an important diagnostic tool for assessing heart functions. In this book, pattern recognition techniques are used for the interpretation of an ECG signal. The techniques used in this pattern recognition application are, signal pre-processing, QRS detection, feature extraction and artificial neural network for signal and cardiac condition (healthy or a certain disease) classification. In this book, the signal processing and neural network toolbox are used in Matlab environment. The processed signal source came from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database which was developed for research in cardiac electrophysiology. Three conditions of ECG waveform were selected from MIT-BIH database in this book. The ECG samples were pre-processed, then features representing the each sample were extracted to produce a set of features that can be used in a neural network to make the classification of samples and the recognition rates were recorded. The book is focused on finding an easy but reliable feature extraction method.