This article presents an advanced continuous wavelet transform (CWT) based approach for fault detection and localization in distribution systems using the artificial neural network (ANN). In this study, CWT extracts distinct features from the transient signals captured from the bus. The derived features are utilized to train and test appropriate ANN architecture in different stages to detect and localize the faults. The proposed scheme provides an optimum method for classification as well as localization of the various kinds of fault with different source short circuit (SSC) level in different locations. The whole detection and localization process consists of several stages. In the first stage, it detects faulty feeder. The faulty line is identified in the second stage. Finally, in the third stage, fault type and fault location are being calculated from the relaying point. The performance of the proposed CWT-ANN based approach is quite promising as compared to traditionally used algorithms. However, a correlation-based feature selection technique is also implemented to reduce training time and improve accuracy. This algorithm is tested in 11 kV radial Indian distribution network but can be applied in other distribution networks also. © 2018, School of Electrical Engineering and Informatics. All rights reserved.