The correct identification of individuals through different biometric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative measures have been proposed during the last two decades: electrocardiogram (ECG) and electroencephalogram (EEG) signals, iris or facial recognition, conductual traits, etc. Several works have shown that ECG-based recognition is a feasible alternative, either for stand-alone or multi-biometric recognition systems. In this paper, we propose a novel framework for ECG-based biometric identification, consisting of a simple and robust feature extraction approach and a clustering-based feature reduction method, that enables for an efficient and scalable biometric identification. The proposed feature reduction approach is a two phase method: it uses a clustering algorithm to group features according to their similarities first, and then clusters are represented in terms of a prototype vector and associated to the available subjects. On its side, the proposed time-domain feature extraction method is a semi-fiducial procedure, where the well-known Pan–Tompkins algorithm is first used to detect the R wave peaks of the QRS complexes, and then fixed-width time segments are selected for further dimensionality reduction and feature extraction. The resulting combined methods are efficient, robust, scalable and attain excellent results (with up-to 98.6% sensitivity) on all the subjects of the Physikalisch-Technische Bundesanstalt (PTB) database, regardless of their pathological or healthy status. Additionally, we also show how the existing Auto Correlation/Discrete Cosine Transform (AC/DCT)-based non-fiducial feature extraction method can be integrated within our framework, allowing us to attain up to 90.6% sensitivity on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Since this database is much noisier and has a much lower sampling rate (360 Hz instead of 1000 Hz), we claim that this is a very good result. •We propose a novel efficient framework for ECG-based biometric identification.•A clustering-based classifier is used to reduce the computational/storage cost.•Hierarchical agglomerative clustering (HAC) is used to build the clusters.•A novel semi-fiducial time-domain feature extraction method is proposed.•Statistical analysis of P-QRS-T complexes is performed on MIT-BIH arrhythmia and PTB.