In this paper we describe an empirical study of human-human multi-tasking dialogues (MTD), where people perform multiple verbal tasks overlapped in time. We examined how conversants switch from the ongoing task to a real-time task. We found that 1) conversants use discourse markers and prosodie cues to signal task switching, similar to how they signal topic shifts in single-tasking speech; 2) conversants strive to switch tasks at a less disruptive place; and 3) where they cannot, they exert additional effort (even higher pitch) to signal the task switching. Our machine learning experiment also shows that task switching can be reliably recognized using discourse context and normalized pitch. These findings will provide guidelines for building future speech interfaces to support multi-tasking dialogue.