Machine learning (ML) is a data-driven approach to discovering patterns and knowledge, and it is different from the physics-based approach, which uses the principles of physics to describe a phenomenon. Physics-based approach has dominated the field of engine diagnostics because of the maturity of scientific and engineering knowledge embodied in the design and manufacturing of the engine and its components. Nevertheless, development of ML techniques has accelerated in the last three decades, and the techniques can potentially lower development time and are applicable to a wide variety of industries. This paper examines some of the most commonly cited ML techniques for handling numerical data and applies them to a gas turbine engine diagnostic problem. The diagnostic problem is to isolate the symptom of engine performance degradations to a root-cause fault or failure. This fault isolation problem is a type of classification problem in the ML world. A hypothetical engine model for commercial airplanes is used in simulation to create a standard dataset. This dataset is then used by all of the selected techniques. The results from the ML algorithms are evaluated in terms of classification accuracy and misclassification rates.