The detecting heartbeat abnormalities (i.e. arrhythmia) depends mainly on the examination of ECG signals over an adequate sampling period. This sampling period needs to contain sufficient data that can be extracted as features. Such features provide accurate measures for the diagnosis of heart arrhythmias. The problem, however, is that the analysis of ECG data requires to properly detect arrhythmias requires many ECG samples to be collected from patients and requires the extract of many features (e.g. temporal or morphological properties). In this paper, we introduce a neural network based solution that can detect heartbeat abnormalities with aim to minimize the feature-set required during the analysis process. Throughout the paper, we present results from testing our neural network using the MIT/BIH Arrhythmia database which show an accuracy rate of 98.70% success rate. We also provide insights to efficiently classify heartbeat rhythms as normal, bradycardia or tachycardia.