The objective of this dissertation is implementing an algorithm for automatic analysis of electrocardiographic signals, using Artificial Neural Networks. The system is divided into several subprograms that extract relevant information about the cardiac signal measured from patients, and points out possible abnormalities by comparison with normal values found in biomedical bibliography. The algorithm uses 4 seconds of the electrocardiogram signal for an initial classification, verifying the feasibility of information extraction. If the extraction is possible, the separate cardiac cycles are collected from the signal and quantitative values for the various components are determined. Finally, these values are compared with the normal values, indicating alterations of wave morphology. This algorithm has a clear relevance in low-income communities, being useful for an initial classification of the patients, being then forwarded to a cardiologist when ECG abnormalities are identified. Another potential use is in helping the cardiologist to automatically determine accurate values from the electrocardiographic register. The results can by considered satistactory, because the values are being compatible with their nature, mainly due to problems of low signal-to-noise ratio in analysed signals. For verification of the results, one metric used was the MAPE, obtaining 19,44 percent for the P wave, 4,85 percent for the QRS complex, 8,93 percent for the begining of the T wave and 7,76 percent for the end of T wave. Another metric used for comparing results with another article, was the Arithmetic Mean/Standard Deviation, obtaining u=-0,8264 ms and ó=3,7037 ms for the onset of the P wave, u=-1,5082 ms and ó=2,2890 ms for the offset of P wave, u=-0,2104 ms and ó=3,2486 ms for the onset of the QRS complex, u=-0,4309 ms and ó=3,9542 ms for the offset of the QRS complex, u=-0,1926 ms and ó=5,7413 ms for the onset of the T wave, u=-0,3346 ms and ó=6,3991 ms for the offset of the T wave.