In composite structures, low-velocity impact-induced damage such as delamination are mostly hidden inside laminates or leave a small dent on the impact side. Thus, detecting this type of damage using conventional inspection methods is not easy and requires much time and cost. To enhance efficiency of these methods, information on the estimated impact locations must be provided with a high accuracy. In this way, unnecessary inspections for large intact regions can be reduced. In this study, impact localization algorithms for various composite structures were developed using the impact induced acoustic signals acquired by multiplexed fiber Bragg grating (FBG) sensors. The acoustic waves from a given impact were transmitted to each FBG sensor, and incurred FBG wavelength shifts were captured by a high speed multiplexible FBG interrogation system with a sampling frequency of 100?kHz. After acquisition of the FBG sensor signals at all the training points in the target section, the impact wave arrival time differences between each FBG signal were calculated to produce the input data sets for neural network training. To reliably use the neural network algorithm for impact identification, high reproducible arrival time determination algorithms are essentially required. In this study, such arrival time determination algorithms were developed through various types of structures. Finally, we evaluated the performances of the suggested impact identification algorithms for a composite wing box structure.