This paper shows a feature extraction method for electrocardiographic signals (ECG) based on dynamic programming algorithms. Specifi cally, we applied local alignment technique for recognition of template in continuous ECG signals. First, we encoded the signal to characters based on the sign and magnitude of fi rst derivative, then we applied local alignment algorithm to search for a complex PQRST template in target continuous ECG signal. Finally, we arrange the data for direct measurement of morphological features in all PQRST segment detected. To validate these algorithms, we contrasted them with conventional analysis by measuring QT segments in MIT’s data base1. We obtained processing time at least 100 times lower than those obtained via conventional manual analysis and error rates in QT measurement below 5%. The automated massive analysis of ECG presented in this work is suitable for post-processing methods like data mining, classifi cation, and assisted diagnosis of cardiac pathologies.