Operators’ knowledge during abnormal situations that are faced in chemical process industries is critical to ensure safety. Operators expand their knowledge base through training programmes that assess their comprehension and skills using simple success and failure criteria, process-based measures, and operator actions. However, these assessment techniques often overlook factors relevant to the evaluation of their cognitive capabilities such as information acquisition pattern, cognitive workload and decision-making strategy. In this work, we present a methodology for evaluating operators’ performance during training that blends process-based measurements with eye-tracking-derived cognitive behaviour. Our methodology is based on Self- Organizing Map (SOM), an unsupervised neural network that allows optimum visualization of complex data. Accordingly, we trained two different SOM networks, one using the process data and the other using eye-tracking data to obtain information about operators’ performance during training. Results indicate that when operators learn the process dynamics, the number of neuronal clusters hit by the process as well as operators’ eye gaze trajectory decrease. The decrease in the number of clusters on SOM trained using process data indicates improved operator performance in terms of successful completion of the task and correct control action with appropriate magnitude. On the other hand, the decrease in the number of clusters hit on SOM trained using eye gaze data signifies that the operator attends to only a few regions on HMI that are critical to the current disturbance/abnormality in the process. Thus, the proposed methodology can be used to gauge the operators’ learning progress during training to understand the transition from novice to expert. © 2022 Elsevier B.V.