This study applies the RAWFS heuristic to the process control setting and identifies the decision-making strategies used by nuclear power plants’ control room operating teams when minimizing the uncertainty involved in emergency operation. The RAWFS heuristic assumes that decision makers in naturalistic settings use the strategies of (1) Reduction, (2) Assumption-based reasoning, (3) Forestalling, (4) Suppression, and (5) Weighing pros and cons of options, for coping with three different types of uncertainties: (a) inadequate understanding, (b) incomplete information, and (c) undifferentiated action. The study relies on the extension of the RAWFS heuristic proposed by van den Heuvel, Alison, and Power (2014) and tests the applicability of this decision-making model on three simulated accident responses observed at the Halden Human-Machine Laboratory. Over six hours of empirical data and more than three hundred identified pairings of uncertainty-strategies are analyzed. The results indicate that with some modifications the RAFWS heuristics is applicable to the field of process control emergency operation and provides a precise understanding of actual decision making in accidental conditions that is key for anticipating possible decision biases as well as for developing effective decision support systems.