A fiber-optical perimeter intrusion detection system based on high-sensitivity FBG vibrating sensors with intelligent identification function was proposed. Stasitical features with an adaptive dynamic threshold were extracted in the time domain first. By inputting the extracted features into a three-layer Back-Propagation Artificial Neural Network classifier, the type of the event can be descerned and determined. The simulated signals and acquired data in the test field were both tested to validate the effectiveness of the proposed system and its identification method. The results show that the average identification rate of the system for the simulation signals can be up to 100%, and that for the actually acquired signals, the average identification rate of the system achieves 96.83%.