In this study, micro-electromechanicals (MEMS) system sensors were used to extract physical signals from a machine tool. A performance assessment was carried out using fuzzy logic theory. This, in turn, was used to judge the ownership of various signals. It then defined the level that each ownership group belonged to in order to determine whether the performance was broken or not. If normal, the system determined specified rules for such normality. During the process of extracting the vibration and noise signals, the system used Fourier transform to analyze any changes made to each signal in the frequency field, and then principal component analysis was used to decrease the data dimensions. We then evaluated the work status of the machine tool on the basis of the signal features. Furthermore, we built a feature math model from the recorded signals using a back propagation neural network and further determined the abnormal items using an error function. Finally, we obtained a diagnostic feature for the performance of the machine tool using physical signals through diagnostic reports from a human-machine interface. The machine tool diagnostic system is able to provide maintenance personnel with a proper way of responding quickly to any reduction in the output from the machine tool so as to avoid further damage.