Electrocardiogram (ECG) is a key diagnostic tool to visualize the heart's activity and to study its normal or abnormal functioning. Physicians perform routine diagnosis by visually examining the shapes of ECG waveform. However, automatic processing and classification of ECG data would be extremely useful in patient monitoring and telemedicine systems. Such realtime applications require techniques that are highly accurate and very efficient. Most of the literature on ECG data rely on timing based features for heartbeat classification. This paper presents a shape or template based method to classify heartbeats as Normal vs. Premature Ventricular Contraction (PVC) beats which is capable of being implemented on low computing, low power consuming and low cost mobile devices such as smartphones. Data analysis is based on MIT-BIH Arrhythmia Database containing 48 Holter recordings of different patients. An overall accuracy of 91% was achieved using the proposed method, which is quite significant considering more than 40,000 heartbeats were analysed. Furthermore, it was observed that only 3 patients with peculiar recordings had significantly low accuracies. Excluding these recordings increased the overall accuracy to 97%. Atypical nature of these recordings was closely investigated to elicit ideas for future work.