Electrocardiographic (ECG) signals record the heart’s electrical activity over time. These signals have typically been assessed for clinical purposes providing a fair evaluation of the heart’s condition. However, it has been shown recently that they also convey distinctive information that can be used for user identification. In this paper we explore these signals for user identification purposes, proposing two data representation and processing techniques based on principal component analysis (PCA) and classification based on the K-NN rule. We analyze and compare these techniques, showing experimentally that 100% identification rates can be achieved. The analysis covers an outlier removal procedure and different configurations of algorithmic and proposed system parameters.