Using social science methods to induce a state of frustration in users, we collect ed physiological, video and behavioral data, and developed a strategy for coupling these data with r eal-world events. The effectiveness of the proposed strategy was tested in a study with thirty-six s ubjects, where the system was shown to reliably synchronize and gather data for affect analysis. Hid den Markov Models were applied to each subject's physiological signals of skin conductivity and bloo d volume pressure in an effort to see if regimes of likely frustration could be automatically discrimin ated from regimes when all was proceeding smoothly. This pattern recognition approach correctly classifi ed these two regimes 67.4% of the time. Mouse-clicking behavior was also synchronized to frustration -eliciting events, and analyzed, revealing NN distinct patterns of clicking responses Compressed Postscript . PDF . Full list of tech reports