Summary In relatively short-term studies it is often difficult to identify the factors that are important in determining the patterns of emergence for different weed species. When results from longer-term studies are averaged over years, they demonstrate that some weed species follow characteristic, and potentially predictable, patterns of annual emergence. In recent years, our understanding of the seasonal changes in dormancy and germination behaviour, and the interaction of these processes with the environment, has advanced considerably. In particular, the capacity for computer-based statistical analysis and the ability to handle large datasets has made it possible to use this information in a predictive way. Currently, there are several approaches to developing predictive weed emergence modelling. Some researchers have taken an empirical approach, seeking to identify correlations between environmental variables and observed emergence patterns. Others have taken a reductionist approach, subdividing the emergence process into its component stages of dormancy, germination and pre-emergence growth, to work towards achieving an eventual understanding of the physiological processes involved. Despite recent advances, a number of major challenges remain (e.g. population variability, dormancy and the quality of the input data) that must be overcome before these emergence models can be implemented in practice. The level of complexity and degree of parameterization incorporated within such models must also be addressed in relation to the intended use of the model.