Multi-scale models of processing systems offer an attractive alternative to the conventional models in the time or frequency domain for process simulation, estimation and control. Defined on trees, these models capture the essential features of the systems’ dynamic behavior, localized in time and scale. In this paper we introduce a formal framework for the formulation of multi-scale models, which leads naturally to a multi-scale systems theory with ensuing definitions for transfer functions, stability, controllability and observability notions. The resulting formulations of (a) system identification, and (b) model predictive control, offer certain very attractive properties, e.g. low computational complexity, natural integration of measurements and control actions at different scales, modeling errors and disturbance descriptions at various scales, handling of multi-rate monitoring and control, and other. In this paper we will provide a brief introduction on how to construct multi-scale models and how they can be used in process estimation and control, summarize some of the key results, and sketch the directions for further work.