We define the inductive process modeling task as the automated construction of quantitative process models from time series and background knowledge. In this task, the background knowledge comprises generic processes that along with a given set of entities define the space of candidate model structures. Typically this space grows exponentially with the size of the library, so past research introduced a hierarchical organization on the processes to constrain that space to a limited set of plausible configurations. However, organizing the processes into a hierarchy takes considerable effort, leads to implicit constraints, and creates a complex relationship between the knowledge of what processes exist and the knowledge of how one can combine them. To address these problems, we developed SC-IPM, an inductive process modeler that uses declarative constraints to reduce the size of the model structure space. In this paper, we describe the constraint formalism and how it guides SC-IPM’s search.