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A major problem in risk assessment is the quantification of uncertainties. A probabilistic model was developed to consider uncertainties in the effect assessment of hazardous substances at the workplace. Distributions for extrapolation factors (time extrapolation, inter- and intraspecies extrapolation) were determined on the basis of appropriate empirical data. Together with the distribution for the benchmark dose obtained from substance-specific dose-response modelling for the exemplary substances 2,4,4-trimethylpentene (TMP) and aniline, they represent the input distributions for probabilistic modelling. These distributions were combined by Monte Carlo simulation. The resulting target distribution describes the probability that an aspired protection level for workers is achieved at a certain dose and the uncertainty associated with the assessment. In the case of aniline, substance-specific data on differences in susceptibility (between species; among humans due to genetic polymorphisms of N-acetyltransferase) were integrated in the model. Medians of the obtained target distributions of the basic models for TMP and aniline, but not of the specific aniline model are similar to deterministically derived reference values. Differences of more than one order of magnitude between the medians and the 5th percentile of the target distributions indicate substantial uncertainty associated with the effect assessment of these substances. The probabilistic effect assessment model proves to be a practical tool to integrate quantitative information on uncertainty and variability in hazard characterisation.