We have applied speech recognition and text-mining technologies to a set of 522 recorded outbound marketing calls and analyzed the results. Since speaker-independent speech recognition technology results in a significantly lower recognition rate than that found when the recognizer is trained for a particular speaker, we applied a number of post-processing algorithms to the output of the recognizer to render it suitable for the Textract text mining system. We indexed the call transcripts using a search engine, used Textract, and associated Java technologies to place the relevant terms for each document in a relational database. Following a search query, we generated a thumbnail display of the results of each call with the salient terms highlighted. We illustrate these results and discuss their utility. We describe a distinct document genre based on the note-taking concept of document content, and propose a significant new method for measuring speech recognition accuracy.