One viewpoint of a knowledge network is a knowledge map that clusters similar knowledge sources into knowledge domains. What is needed is an automatic mapping tool that 1) takes the knowledge sources, 2) creates a conceptual map of the domain space, 3) clusters like sources, and 4) places them together on the map. This research (in progress) is an attempt to determine the value of the Kohonen Self-Organizing Map for use as an interactive textual knowledge mapping tool for categorization of large sets of textual knowledge sources. Initial results have shown the algorithm to be promising in the area of creating a conceptual map of the document space, but it has been less successful at the task of clustering and assigning documents within categories. The purpose of this paper is to quantify the Kohonen algorithm's ability to cluster similar documents and to explore possible improvements to it.