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Identification of low-velocity impacts to composite structures has become increasingly important in the aerospace industry. Knowing when impacts have occurred would allow inspections to be scheduled only when necessary, and knowing the approximate impact location would allow for a localized search, saving time and expense. Additionally, an estimation of the impact magnitude could be used for damage prediction. This study experimentally investigated a methodology for impact identification. To achieve the approach, the following issues were covered in this study: impact detecting; signal processing; feature extraction; impact identification. In impact detection, them smart structures, two piezoelectric sensors embedded in composite structures, are designed to measure impact signals caused by foreign object impact events. The impact signals were stored in computer system memory through the impact monitoring system developed in this study. In signal processing, the cross correlation method was used to process the measured impact signals. This processing built the correlation between the impact signals and location of impacts as well as impact magnitude. In feature extraction, the initial feature data were gained from the cross correlation results through the point and segmentation processing. The final feature data were selected from the initial feature data with a fuzzy clustering method. In impact identification, the adaptive neuro fuzzy inference systems (ANFIS) were built with the feature data to identify abscissas of impact location, ordinates of impact location and impact magnitude. The parameters of the ANFISs were refined with a hybrid learning rule i.e. the combination of the Least Square Estimation and the Steepest Descent algorithm. Real time software developed in Visual Basic code manipulated the monitoring and control system for the impact experiments. Also a software package developed with MATLAB, implemented the impact identification and the system simulation.