This study investigates the feasibility of using electrocardiogram (ECG) signals as a biometric recognition trait, offering an alternative to traditional methods such as passwords and fingerprint scanning. It leverages ECG’s inherent liveness detection, high security, and unique physiological properties to develop a robust identification system. Using data collected from three sessions over two weeks, the ECG signals were processed through filtering and normalization before feature extraction and the application of various machine learning classifiers. The experiments evaluated the impact of temporal separation between training and testing sessions, revealing a significant drop in accuracy with increased time intervals. To mitigate this, the effectiveness of multi-session training was explored, improving the system performance. The trade-off between the number of heartbeats used for training and identification accuracy was also assessed, showing that while more heartbeats generally led to higher accuracy, the marginal gains did not justify the extended acquisition time in real-world scenarios. Linear Discriminant Analysis (LDA) emerged as the most effective classifier, consistently outperforming others in various scenarios. The obtained results underscore the potential of ECG-based biometrics in secure identification systems, particularly when leveraging data from multiple sessions.