We investigate the problem of text segmentation by topic. Applications for this task include topic tracking of broadcast speech data and topic identification in full-text databases. Researchers have tackled similar problems before but with different goals. This study focuses on data with relatively small segment sizes and for which within-segment sentences have relatively few words in common making the problem challenging. We present a method for segmentation which makes use of a query expansion technique to find common features for the topic segments. Experiments with the technique show that it can be effective.