This paper investigates the potential of monitoring the physiological stress levels of human pilots in real-time. This investigation considers the use of wearable technology to measure electrodermal activity, skin temperature, and heart rate to assess the current stress level of an individual. Stimuli in the form of static images were selected from the International Affective Picture System (IAPS) in order to invoke stressful or calming feelings. Metrics were selected from the measured data to analyze differences between the stress and non-stress groups. Additionally, machine learning techniques were explored to classify the stress or non-stress state based on the physiological data. The preliminary results of this study indicated significant limitations in the use of this data for accurately detecting a high-stress state. Future work will consider a refined data collection protocol and increased sample size in order to generalize the result.