A predictive model based on growth of Listeria monocytogenes in milk is described. The main aim of this work was to generate a predictive model in milk acidified with lactic acid to mimic conditions found in a range of dairy products. A complete factorial design was employed to determine the effects of pH (4.5-7.5), temperature (3-35 degrees C) and salt concentration (0-8%) on growth of the organism. There were 210 design points and growth curves were individually fitted for the Gompertz function using non-linear regression. Descriptors of the curves, such as lag phase duration (LPD), exponential growth rate (EGR) and generation time (GT) were calculated and polynomial models were developed relating these to pH, temperature and salt concentration. The selected cubic polynomial model gave acceptable predictive estimates of growth and was stable, i.e. predictions were repeatable over the range of environmental variables studied. The model was further tested to determine its capacity for predicting growth of listeria in a range of dairy foods and these validation studies confirm its usefulness as a rapid means of estimating growth of the organism under specified environmental conditions.