In this paper we present results on real data, focusing on personal identification based on one lead ECG, using a reduced number of heartbeat waveforms. A wide range of features can be used to characterize the ECG signal trace with application to personal identification. We apply feature selection (FS) to the problem with the dual purpose of improving the recognition rate and reducing data dimensionality. A feature subspace ensemble method (FSE) is described which uses an association between FS and parallel classifier combination techniques to overcome some FS difficulties. With this approach, the discriminative information provided by multiple feature subspaces, determined by means of FS, contributes to the global classification system decision leading to improved classification performance. Furthermore, by considering more than one heartbeat waveform in the decision process through sequential classifier combination, higher recognition rates were obtained.