Voltage sag characteristics are subject to change owing to system short-circuit rating in situations where DG's contribution to power injection during fault events is considered. Therefore, the fast growth in the deployment of DG leads to an analysis of its impact on system operation and reliability. A methodology for characterization and identification of voltage sags and swells caused by faults in a distribution system with distributed generation (DG) is presented in this paper. The proposed methodology, involving Wavelet transform processing technique and a neural network classifier, yields a computational tool applied to IEEE 13-Node Test Feeder. This methodology provides a way to handle high data stream to obtain a power system response generalization to voltage disturbances.