In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.