In this paper we present a context-sensing component that recognizes meetings in a typical office environment. Our prototype detects the meeting start and end by combining outputs from pressure and motion sensors installed on the chairs. We developed a telephone controller application that transfers incoming calls to voice-mail when the user is in a meeting. Our experiments show that it is feasible to detect high-level context changes with “good enough” accuracy, using low-cost, off-the-shelf hardware, and simple algorithms without complex training. We also note the need for better metrics to measure context detection performance, other than just accuracy. We propose several metrics appropriate for our application in this paper. It may be useful, however, for the community to define a set of general metrics as a basis to compare different approaches of context detection.