Quantitative microbiological risk assessment is a very new and unique scientific approach able to link, for the first time, data from food (in the farm-to-fork continuum) and the various data on human disease to provide a clear estimation of the impact of contaminated food on human public health. The Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO) have recently launched risk assessment studies of a number of pathogen–food commodity combinations (Salmonella in eggs and in broiler chickens, Listeria monocytogenes in ready-to-eat foods, Campylobacter in broiler chickens, Vibrio in seafood) to be used to lower the risk associated with these food-borne diseases and ensure fair practices in the international trade of food. The FAO/WHO Listeria risk assessment was undertaken in part to determine how previously developed risk assessments done at the national level could be adapted or expanded to address concerns related to L. monocytogenes in ready-to-eat foods at an international level. In addition, after initiation of the risk assessment, the risk assessors were asked by the Codex Committee on Food to consider three specific questions related to ready-to-eat foods in general, which are: (1) estimate the risk for consumers in different susceptible populations groups (elderly, infants, pregnant women and immunocompromised patients) relative to the general population; (2) estimate the risk for L. monocytogenes in foods that support growth and foods that do not support growth under specific storage and shelf-life conditions; (3) estimate the risk from L. monocytogenes in food when the number of organisms ranges from absence in 25 g to 1000 colonies forming units per gram or milliliter, or does not exceed specified levels at the point of consumption. To achieve these goals, new dose–response relationships and exposure assessments for ready-to-eat foods were developed. Preliminary data indicate that eliminating the higher dose levels at the time of consumption has a large impact on the number of predicted cases.