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The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However, the pipeline leakages become a major concern as their failure may contribute to operational and economic loss as well as environmental pollution. This paper proposed a system to detect pipe fault at different locations. Empirical Mode Decomposition (EMD) was applied for feature extraction using energy and kurtosis. The one-against-one (OAO) and one-against-all (OAA) multiclass SVM with radial basis function (RBF), polynomial and sigmoid kernel functions were implemented in order to classify the multiple fault locations from the extracted features. RBF kernel function recorded the highest classification accuracy …