An Electrocardiogram (ECG) is a test that records the electrical activity of the heart to locate the abnormalities. Automatic ECG classification is an emerging tool for the cardiologists in medical diagnosis for effective treatments. In this paper, we propose efficient techniques to automatically classify the ECG signals into normal and arrhythmia affected (abnormal) category. For these categories morphological features are extracted to exemplify the ECG signal. Probabilistic neural network (PNN) is the modeling technique engaged to capture the distribution of the feature vectors for classification and the performance is calculated. ECG time series signals in this work are collected from MIT-BIH arrhythmia database. The proposed an accurately classify and discriminate the difference between normal ECG signal and arrhythmia affected signal with 96.5% accuracy.