As speech recognition moves toward more unconstrained domains such as conversational speech, we encounter a need to be able to segment (or resegment) waveforms and recognizer output into linguistically meaningful units, such a sentences. Toward this end, we present a simple automatic segmenter of transcripts based on N-gram language modeling. We also study the relevance of several word-level features for segmentation performance. Using only word-level information, we achieve 85% recall and 70% precision on linguistic boundary detection.