This paper describes the identification of chemical processes with multiple neural network models. This concept is called a 'multimodel' approach. The multimodel approach presents a flexible framework which allows the integration of other model paradigms. Three different methods to construct neural network multimodels are presented. First, a priori knowledge is used to decompose the input domain into operating regimes. Each regime is modelled by a different neural network. In the second multimodel approach, unsupervised learning in form of clustering and SOMs is used to split the input domain. The third approach uses a gating neural network to divide the input space. In contrast to the first two approaches, the non-linear gated network approach allows the multimodel to simultaneously learn a suitable decomposition and the mapping in each regime. All three approaches are evaluated for a fed-batch fermentation.