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Depending on the data available, gas turbine diagnostics may be performed in various ways. For model based diagnostics methods, the most common data to use is from steady state operation. Regarding the amount of data, the smallest dataset comprise of a single operating condition for a specific gas turbine. The other end of the scale considers a fleet of engines, where data can be utilized for cross comparisons and identification of deviations in engine operation. In-between these two extremes, twin-engine airplanes can be utilized to obtain additional diagnostic information. In this paper, a multi-point diagnostic method for a twin-engine airplane is developed and evaluated. It is based on the assumption that one engine can be operated individually for data collection, by varying the bleed flow extraction, while the other engine supply the airplane subsystems with the required bleed flow during the data collection time. The collected data then goes into a multi-point optimization which minimizes the difference between the health parameter estimations. From the health parameters, measurements corresponding to a reference operating condition are obtained and used by a data-driven classifier for fault identification and isolation. The method has proven to be able to detect both single and double component faults with high accuracy for the evaluated dataset.