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A method is presented to predict diffusion coefficients in polyolefins using stochastic modelling. A large number of experimental diffusion coefficients, published in the literature as one dataset, was used to derive probability distributions of diffusion coefficients in the polymers low-density polyethylene and linear low-density polyethylene, medium-and high-density polyethylene, and polypropylene. An equation is proposed to describe the diffusion coefficient as a function of the molar mass of the migrant. Model parameters and standard deviations are predicted by minimizing the sum of squared errors and the residuals are used to check the assumed types of probability distribution. The experimental data can be described by a log-normal distribution. It is shown how the derived probability distributions can be used as input for migration predictions. The method presented provides information about the most likely migration results for a given packaging-food simulant combination. This is important for prediction of the probability that a given migration limit may be exceeded. INTRODUCTIONResearch during the second half of the nineteenth and first half of the twentieth century, as reviewed by Crank and Park, 1 shows that the transport of small molecules through polymers can be described as a diffusion process. This rate of this process depends on the value of the diffusion coefficient and the distribution of the small molecular compound between the polymer and the contacting phase (usually expressed as a partition or solubility coefficient). This was later shown also to be the case for migration of larger molecules (such as monomers and additives) from plastic packaging materials to foods or food simulants. 2 -5 By using an appropriate diffusion model, it is possible to predict migration as a function of time. Such models are useful for predicting exposure risks of toxic compounds in packaging materials and may in some cases replace time-consuming and expensive migration experiments currently required for demonstrating compliance with food packaging regulations. The key question, however, is how to obtain values for the model parameters that are characteristic of each combination of migrant, packaging material and food simulant. A priori prediction from physical properties alone is impossible, since the precise factors determining migration are not known. The