A method is proposed for the modeling, observation, and control of processes characterized by sequential transformation steps. Using the method, such processes are described by a battery of alternative submodels, each of which qualitatively represents one of the process steps. The submodels cannot themselves track transitions from step to step. This is handled by switching to an alternative submodel with a significantly better predictive performance than that presently in use. The main advantage of the proposed approach is that each submodel is significantly simpler than the full model of the system. Example applications demonstrate that these submodels, when combined with the switching strategy, are also convenient for parameter estimation, state observation, and regulatory control.