Background and Objectives: In recent decades, several major accidents have occurred in high-reliability industries such as petrochemical companies. Accident analysis shows that the occurrence of more than 90% of accidents in industries are due to human factor and only with technical-engineering measures and the establishment of safety rules and regulations can not be institutionalized safe behaviors in such industries. Therefore, despite a slight reduction in human presence in these industries, the potential for human error risks is still high. The aim of this study was to identify and assess human errors in a petrochemical plant using the technique for the retrospective and predictive analysis of cognitive errors (TRACEr). Methods:The sample size was all the eight operators of control room working in four shifts. In the first step, all tasks were analyzed using the hierarchical task analysis in order to identify sub-tasks. Then, for all the subtasks, different error modes (external and internal), psychological error mechanism (PEM) and performance shaping factors (PSFs) were identified and recorded in TRACEr sheet. Results:The analysis of TRACEr sheets indicated that of a total number of 1171 detected errors, the internal and external errors were 50.67% (n=593) and 49.33% (n=578), respectively. In this line, ̔ timing/sequence̕ errors with 35.36% and 'quality/selection' errors with 30.03% were identified as the highest and lowest external error modes, respectively. In classifying the internal error modes, action errors with 31.87% and decision making with 10.73% were identified as the highest and lowest external error modes, respectively. Within PEMs, ̔ distraction/preoccupation̓ (23.61%) was identified as the main causes of perception errors. The analysis of the PSFs shows that 'Organization' with 27.95% and 'task complexity' with 8.74% were two main factors affecting the task errors. Conclusion:The current study could identify many of the errors and conditions that affect the performance of operators. Therefore, this study can be introduced as a basis for managers and stockholders of chemical industries with complexity and high risk in order to prioritize human error prevention programs.