In this paper we present a novel system which automatically converts text documents into XML by extracting information from previously tagged XML documents. The system uses the Self-Organizing Map (SOM) learning algorithm to arrange tagged documents on a two-dimensional map such that nearby locations contain similar documents. It then employs the inductive learning algorithm C5.0 to automatically extract and apply auto-tagging rules from the nearest SOM neighbours of an untagged document. The system is designed to be adaptive, so that once a document is tagged in XML, it learns from its errors in order to improve accuracy. The automatically tagged documents can be categorized on the SOM, further improving the map’s resolution. Various experiments were carried out on our system, using documents from a number of different domains. The results show that our approach performs well with impressive accuracy.