Homeland security researchers and analysts more than ever must process large volumes of textual information. Information extraction techniques have been proposed to help alleviate the burden of information overload. Information extraction techniques, however, require re-training and/or knowledge re-engineering when document types vary as in the homeland security domain. Also, while effectively reducing the volume of the information, information extraction techniques do not point researchers to unanticipated interesting relationships identified within the text. We present the Arizona TerrorNet, a system that utilizes less specified information extraction rules to extract less choreographed relationships between known terrorists. Extracted relations are combined in a network and visualized using a network visualizer. We processed 200 unseen documents using the TerrorNet which extracted over 200 relationships between known terrorists. An Al Qaeda network expert made a preliminary inspection of the network and confirmed many of the network links.