The paper presents the use of different approaches to engine health assessment using on-wing data obtained over a year from an engine of a commercial short-range aircraft. The on-wing measurements are analyzed with three different approaches, two of which employ two models of different quality. Initially, the measurements are used as the sole source of information and are postprocessed utilizing a simple “model” (a table of corrected parameter values at different engine power levels) to obtain diagnostic information. Next, suitable engine models are built utilizing a semi-automated method which allows for quick and efficient creation of engine models adapted to specific data. Two engine models are created, one based on publicly available data and one adapted to engine specific on-wing “healthy” data. These models of different details are used in a specific diagnostic process employing model-based diagnostic methods, namely the probabilistic neural network (PNN) method and the deterioration tracking method. The results demonstrate the level of diagnostic information that can be obtained for this set of data from each approach (raw data, generic engine model or adapted to measurements engine model). A subsystem fault is correctly identified utilizing the diagnostic process combined with the engine specific model while the deterioration tracking method provides additional information about engine deterioration.