We examine the distribution of overlapping speech in large multi-party conversations, including two different types of meetings, and two corpora of telephone conversations. Analyses are based on forced alignment and speech recognition using an identical recognizer across tasks. Three results are discussed. First, all corpora show high overall rates of overlap, with similar rates for meetings and telephone conversations. Second, speech recognition performance in non-overlapped regions of meetings is no worse than that for single-channel telephone conversations, while recognition in overlap regions degrades considerably. Finally, interrupt locations are associated with endpoints of word-level events in a speaker's turn, including back-channels, discourse markers, and disfluencies. Results suggest that overlaps are an important inherent characteristic of conversational speech that should not be ignored; on the contrary, they should be jointly modeled with acoustic and language model information in machine processing of conversation.