The emerging research area of Affective Computing seeks to advance the field of Human-Computer Interaction (HCI) by enabling computers to interact with users in ways appropriate to their affective states. Affect recognition, including the use of psychophysiologcal measures (e.g. heart rate), facial expressions, speech recognition etc. to derive an assessment of user affective state based on factors from the current task context, is an important foundation required for the development of Affective Computing. Our research focuses on the use of three physiological signals: Blood Volume Pulse (BVP), Galvanic Skin Response (GSR) and Pupil Diameter (PD), to automatically monitor the level of stress in computer users. This paper reports on the hardware and software instrumentation development and signal processing approach used to detect the stress level of a subject interacting with a computer, within the framework of a specific experimental task, which is called the 'Stroop Test'. For this experiment, a computer game was implemented and adapted to make the subject experience the Stroop Effect, evoked by the mismatch between the font color and the meaning of a certain word (name of a color) displayed, while his/her BVP, GSR and PD signals were continuously recorded. Several data processing techniques were applied to extract effective attributes of the stress level of the subjects throughout the experiment. Current results indicate that there exists interesting similarity among changes in those three signals and the shift in the emotional states when stress stimuli are applied to the interaction environment.