In this paper, we propose an intelligent network surveillance system to protect the urban underground pipelines from external damages caused by excavation devices. At each monitoring site, a microphone array is implemented for real-time acoustic collection and an intelligent excavation device recognition algorithm is embedded. A surveillance platform built on the fusion of multi monitoring sites is designed for a whole city. A novel statistical feature extraction method is first developed to mining the useful and representative information for the collected acoustic signals. Then, an artificial neural network trained by the popular extreme learning machine (ELM) and the regularized ELM (RELM) is used to perform the recognition of excavation devices in each monitoring site. To show the efficiency of the proposed system, experiments are conducted in this paper. Recognition performance on four most destructive devices is studied.