Predictive models, to estimate the reduction in Escherichia coli O157:H7 concentration in beef burgers, have been developed to inform risk management decisions; no analogous model exists for Salmonella spp. in pork burgers. In this study, "Extra Lean" and "Regular" fat pork minces were inoculated with Salmonella spp. (Salmonella 4,[5],12,i:-, Salmonella Senftenberg and Salmonella Typhimurium) and formed into pork burger patties. Patties were cooked on an electric skillet (to imitate home cooking) to one of seven internal temperatures (46, 49, 52, 55, 58, 61, 64 °C) and Salmonella enumerated. A generalised linear logistic regression model was used to develop a predictive model for the Salmonella concentration based on the internal endpoint temperature. It was estimated that in pork mince with a fat content of 6.1%, Salmonella survival will be decreased by - 0.2407 log10 CFU/g for a 1 °C increase in internal endpoint temperature, with a 5-log10 reduction in Salmonella concentration estimated to occur when the geometric centre temperature reaches 63 °C. The fat content influenced the rate of Salmonella inactivation (P = 0.043), with Salmonella survival increasing as fat content increased, though this effect became negligible as the temperature approached 62 °C. Fat content increased the time required for patties to achieve a specified internal temperature (P = 0.0106 and 0.0309 for linear and quadratic terms respectively), indicating that reduced fat pork mince may reduce the risk of salmonellosis from consumption of pork burgers. Salmonella serovar did not significantly affect the model intercepts (P = 0.86) or slopes (P = 0.10) of the fitted logistic curve. This predictive model can be applied to estimate the reduction in Salmonella in pork burgers after cooking to a specific endpoint temperature and hence to assess food safety risk.