In this study, we developed predictive models describing the growth of Staphylococcus aureus on cooked broccoli florets. A pool of 3.5 log CFU/g of five S. aureus strains were inoculated on 10 g broccoli portions. The samples were then stored at 10, 20, 30 and 37 degrees C, and colonies were enumerated at different time intervals. Baranyi and Roberts model was fitted to the data using a Bayesian Adaptive Markov Chain Monte Carlo for estimation of the growth parameters. S. aureus showed low growth at 10 degrees C on broccoli samples and at 20-37 degrees C interval, Baranyi and Roberts model fitted well to the experimental data (R-2>0.97). Estimated growth parameters were correlated with the possibility of toxin production and indicate the potential presence of these biological hazards on contaminated broccoli after heat treatment. Additionally, linear regression was performed for growth rate as storage temperature function. This secondary model followed a linear tendency with R-2=0.997 and was compared with two tertiary models (ComBase Predictor and Pathogen Modeling Program) and literature data, demonstrating similar growth rate values of both. These results can be helpful for food services and managers to establish food safety standards for S. aureus growth on cooked broccoli.