With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining.