In the processing industries, operating conditions change to meet the requirements of the market and customers. For example, in injection molding, the same machine may be used with different molds and materials to make different parts. For each different mold, material, and machine combination, data-based process modeling must be repeated for the development of a new prediction model. This may involve the repetition of a large number of experiments, if common process characteristics are ignored. Obviously, this is inefficient and uneconomical. Although operating conditions may be different for different processes, certain process behaviors and characteristics are common under these conditions. The effective use and extraction of these common process behaviors and characteristics can allow fewer experiments for the development of new process model, resulting in a savings of time, cost, and effort. With this as the key objective, a modeling method is proposed for process modeling. This includes information extraction from a base model, a design of experiments (DOE). for the new process, assessment of difference, model migration strategy, and verification for the new model. As an illustrative example, this paper demonstrates model development for new molds to predict the width of an injection-molded part, taking advantage of an existing model.