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Product development processes generate large quantities of experimental and analytical data. The data evaluation process is usually quite lengthy since the data needs to be extracted from a large number of individual output files and arranged in suitable formats before they can be compared. When the data quantity grows extremely large, manual extraction cannot be done in a limited timeframe. This paper describes a set of tools developed by MTS engineers to automatically extract the desired information from a large number of files and perform data post-processing. The tools greatly improved both speed and accuracy of the evaluation process during the development of a sound quality-based end-of-line inspection system for seat tracks [1]. It allowed engineers to quickly gather a comprehensive understanding of the relative importance of individual design parameters and of their correlation to the subjective perception of the sound quality of the seat track.