Background: The objective of the present research was to investigate an electroencephalography (EEG) brain-computer interface for monitoring mental workload during virtual reality flight simulation. Most aviation accidents are related to pilot cognition and a mismatch between task demands and cognitive resources. Real-time neurophysiological monitoring that identifies high-workload mental states offers an effective approach for reducing accidents during flight. Method: Non-pilot participants performed simulated flight operations. Workload was manipulated to represent regular flight scenarios by varying navigational difficulty and performing communication tasks. EEG data was collected and used to classify periods of flight as high or medium workload. Results and implications: A classification rate of 75.9% was obtained which provides promise for future use of EEG brain-computer interfaces in aviation practice. The most informative classification features (Alpha and Beta oscillations) may represent components of working memory which corresponds to predictions from a multiple resource theory approach to experimental design.