Low-velocity impact damage is a major concern in the design of structures made of advanced laminated composites, because such damage is mostly hidden inside the laminates and cannot be detected by visual inspection. It is necessary to develop the impact monitoring techniques providing on-line diagnostics of smart composite structures susceptible to impacts. In this paper, I proposed the process for impact location detection in which the generated acoustic signals are detected by PZT using the neural network paradigms. To improve the accuracy and reliability of neural network based impact identification method, the Levenberg-Marquardt algorithm and the generalization methods were applied. This study concentrates not only on the determination of the location of impacts from sensor data, but also the implementation of time-frequency analyses such as the Wavelet Transform (WT) to measure the characteristics of acoustic emission (AE) signals for the determination of the occurrence and the severity of impact damages. These two processes can be operated simultaneously. As a fundamental approach, the characteristics of the AE signals due to matrix cracks and the evolution of free-edge delamination in $[\pm45_2/0_2/90_2]_S$ Gr/Ep laminates under tensile load were analyzed by the WT. The waveform based AE method was discussed to investigate the damage mode and mechanism. Then, the drop-weight type impact test was carried out to investigate the characteristics of impact damages of quasi-isotropic laminates using the WT. This paper shows that the AE signals generated by each damage mode can be distinguished by the WT. The examples of the simultaneous impact monitoring were presented. The cross-ply laminates and I-stiffened quasi-isotropic plate were tested to identify the impact location and the damages for the case of damaging impact loads.