This method, based on fuzzy logic principles, is intended to find the most likely solution of an over-determined system, in specific conditions. The method addresses typical problems encountered in gas turbine performance analysis and, more specifically, to the alignment of a synthesis model with measured data. Generally speaking, the relatively low accuracy of measurements introduces a random noise around the true value of a performance parameter and distorts any deterministic solution of a square matrix-based linear system. The fuzzy logic estimator is able to get very close to the real solution by using additional (pseudo-redundant) parameters and by building the most likely solution based on each of the measurement accuracies. The accuracy—or “quality”—of a measurement is encapsulated within an extra dimension which is defined as fuzzy and which encompasses the whole range of values, between 0 (false) and 1 (true). The value of the method is shown in two examples. The first simulates compressor fouling, the other deals with actual engine test data following a hardware modification. Both examples experience noisy measurements. The method is stable and effective even at high level of noise. The results are within the close vicinity of the expected levels (within 0.2% accuracy) and the accuracy is about ten times lower than the noise level.