This paper presents an investigation on pattern classification techniques applied to voltage sag monitoring data. Similar pattern groups or sets of classes, resulting from a voltage sag classification, represent disturbance categories that may be used as indexes for a cause/effect disturbance analysis. Various classification algorithms are compared in order to establish a classifier design. Results over clustering performance indexes are presented for hierarchical, fuzzy c-means and k-means unsupervised clustering techniques, and a principal component analysis is used for features (or attributes) choice. The efficiency of the algorithms was analyzed by applying the CDI and DBI indexes.