Diagnostic algorithms that use gas path measurements and models are capable to diagnose not only different faults of the gas path itself but also malfunctions of measurement and control systems. Since the variety of gas turbine fault conditions is great, they are joined in a limited number of classes. Different principles to create these classes are known and there are many fault classifications in practice. In an investigation stage, it is difficult to predict what classification will then be applied in a real monitoring system therefore the investigators usually experiment with different fault types and fault numbers. The present paper proposes the approach that allows simple creating multiple classification variations including complex and realistic fault classes. This approach also permits easy changing between the variations and the pattern recognition techniques applied for each variation. As a result of application of each technique to each classification, probability of correct diagnostic decisions and execution time are determined. They are criteria of diagnosis efficiency. In this way, the approach allows studying the influence of fault classification on diagnosis efficiency. The paper performs such a study for a power plant for natural gas pipelines. Twelve classification variations are analyzed with the use of three recognition techniques: Multi-Layer Perceptron, Radial Basis Network and Probabilistic Neural Network. Additionally, a new boundary for fault severity is proposed and investigated by comparing three boundary options.