The proposed method in this paper indicates two issues, selection of discriminative features and classiﬁes event classes with minimum error. Wavelets features (WF) of power quality (PQ) events are extracted using wavelet transform (WT) and fuzzy classiﬁers classify events using these features. The captured signals are often corrupted by noise; the non-linear and non-stationary behaviors of PQ events make the detection and classiﬁcation tasks more cumbersome. Performance comparison of the proposed method is made with three other fuzzy classiﬁers using different wavelets and superiority is veriﬁed. In the proposed approach of event classiﬁcation, fuzzy product aggregation reasoning rule based method has been used.