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Sophistication and complexity of current turbine engines have mandated the need for advanced fault diagnostic for monitoring the health condition of turbine engines. A critical component of these advanced diagnostic systems is the decision fusion software. The purpose of the decision fusion software system is to increase diagnostic reliability, accuracy, and improve safety of the engine operation. It also helps decrease diagnostic false alarms hence save maintenance time. This paper focuses on the development and implementation of decision-fusion software system for enhancing the diagnosis of turbine engines. The paper describes how a fuzzy logic system is used to predict and diagnose turbine engine health conditions at different levels based on the health parameters, i.e., efficiency and flow. In this paper, the decision fusion software system was broken down into two subsystems namely, Decision Making Subsystem (DMS) and Decision Fusion Subsystem (DFS). The goal of the DMS is to predict the health condition of the engine components. While the objective of DFS is to assess the overall health condition of the engine based on information provided by the DMS. The test results of developed fusion software system are promising in providing reliable diagnostics for turbine engine, subsequently reducing maintenance cost. All the system development steps and testing results on the commercial grade turbine engine model C-MAPSS will be presented in this paper. [ABSTRACT FROM PUBLISHER]