This paper presents an approximation to the recognition of phenomena associated to power quality in electric networks, by means of neural networks and based on the work discussed in reference [Taboada, JD, et al., (2003)], where a former recognition of the phenomena was processed using the discrete wavelet transform (DWT). A multiple layer perceptron (MLP) was used, together with the back propagation algorithm for the training process. The patterns recognized corresponded to signals of harmonic, transient, sags and swell waveforms.
