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An effective and reliable gas path diagnostic method that could be used to detect, isolate, and identify gas turbinedegradations is crucial in a gas turbine condition-based maintenance. In this paper, we proposed a new combinedtechnique of artificial neural network and support vector machine for a two-shaft industrial gas turbine engine gaspath diagnostics. To this end, an autoassociative neural network is used as a preprocessor to minimize noise and generatenecessary features, a nested support vector machine to classify gas path faults, and a multilayer perceptron to assess themagnitude of the faults. The necessary data to train and test the method are obtained from a performance model of thecase engine under steady-state operating conditions. The test results indicate that the proposed method can diagnoseboth single- and multiple-component faults successfully and shows a clear advantage over some other methods in termsof multiple fault diagnosis. Moreover, 5-8 sets of measurements have been used to assess the prediction accuracy, andonly a 2.3% difference was observed. This result indicates that the proposed method can be used for multiple faultdiagnosis of gas turbines with limited measurements.