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Anomaly detection provides an early warning of unusual behavior in units in a fleet operating in a dynamic environment by learning system characteristics from normal operational data and flagging any unanticipated or unseen patterns. For a complex system such as an aircraft engine, normal operation might consist of multiple modes in a high dimensional space. Therefore, anomaly detection approaches based on single cluster data structure will not work. This paper investigates data clustering and neural network based approaches for anomaly detection, specifically addressing the situation which normal operation might exhibit multiple hidden modes. Results show detection accuracy can be improved by data clustering or learning the data structure using neural networks.