Model identification from dynamic experimental data may involve the conduction of multiple experiments to identify a suitable model with adequate accuracy. In contrast to the a priori specification of design sets, an iteratively conducted model-based experimental design exploiting previous data promises significantly better results with lower effort. Yet, in dynamic systems, the inputs of the model to be identified often cannot be designed directly, but depend on the experimental degrees of freedom. To allow for model-based de sign for the identification of dynamic hybrid or fully unstructured models, a new design criterion is developed in this work. It is based on an input-space coverage approach and allows to simultaneously design the experiment for multiple models to be identified. Incremental identification is applied to efficiently construct the unknown models from data. The resulting iterative design and identification methodology is illustrated on a reaction kinetic model identification for the acetoacetylation of pyrrole in a simulation study. (C) 2008 Elsevier B.V. All rights reserved.