In this work, inspired by the application of human-machine interaction and the potential use that human-computer interfaces can make of knowledge regarding the affective state of a user, we investigate the problem of sensing and recognizing typical affective experiences that arise when people communicate with computers. In particular, we address the problem of detecting ``frustration'' in human computer interfaces. By first sensing human biophysiological correlates of internal affective states, we proceed to stochastically model the biological time series with Hidden Markov Models to obtain user-dependent recognition systems that learn affective patterns from a set of training data. Labeling criteria to classify the data are discussed, and generalization of the results to a set of unobserved data is evaluated. Significant recognition results (greater than random) are reported for 21 of 24 subjects. Compressed Postscript . PDF . Full list of tech reports