Objective To investigate speech features, human–machine alarms, and operator–system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios. Background Theories and models of cognitive workload are critical for the design and evaluation of human–machine systems. Unfortunately, there are very few nonintrusive cognitive workload measures available for realistic dynamic human–machine interaction. Method The study was conducted in a full-scope control room research simulator of an advanced nuclear reactor. Six crews, each consisting of three operators, participated in 12 scenarios. The operators rated their workload every second minute. Machine learning algorithms were trained to estimate operators’ workload based on crew communication, operator–system interaction, and system alarms. Results Random Forest (RF) utilizing speech and system features achieved an accuracy of 67% on test data. Utilizing speech features only, the accuracy achieved was 63%. The most important speech features were pitch, amplitude, and articulation rate. A 61% accuracy was achieved when alarms and operator–system interaction features were used. The most important features were the number of alarms and amount of operator–system interaction. Accuracy for algorithms trained for each operator ranged from 39% to 98%, with an average of 72%. For a majority of analyses performed, RF and extreme gradient boosting (XGB) outperformed other algorithms. Conclusion The results demonstrate that the features investigated and machine learning models developed provide a potential for the dynamic nonintrusive measurement of cognitive workload. Application The approach presented can be developed for nonintrusive workload measurement in real-world human–machine applications, simulator-based training, and research.