This paper presents an approach for human authentication based on electrocardiogram (ECG) waveforms. ECG data was collected from 24 individuals during the realization of cognitive tests, where subjects held a surface mount triode placed on the V2 pre cordial derivation. Authentication is based on MAP, One-Class and 1-NN classifiers. Results show that ECG-based authentication may be a feasible tool for biometric systems. The One-Class classifier with class normalization has presented enhanced performance, with an equal error rate of 3.5%.