This paper presents the performance evaluation of support vector machine (SVM) with one against all (OAA) and different classification methods for power quality monitoring. The first aim of this study is to investigate EEMD (ensemble empirical mode decomposition) performance and to compare it with classical EMD (empirical mode decomposition) for feature vector extraction and selection of power quality disturbances. Feature vectors are extracted from the sampled power signals with the Hilbert Huang Transform (HHT) technique. HHT is a combination of EEMD and Hilbert transform (HT). The outputs of HHT are intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs, IF, and IA. The ten features-i.e., the mean, standard deviation, singular values, maxima and minima-of both IF and IA are then calculated. These features are normalized along with the inputs of SVM and other classifiers. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.