Although jet milling is a very energy consuming grinding process it is increasingly used in industry because very fine grinding product with a narrow size distribution is attained without contamination as the milling occurs by inter particle collisions. At Delft University of Technology a project has been started to achieve a considerable energy reduction in jet milling processes. The grinding plant consists of a spiral jet mill in closed loop with an external classifier. Main feature of the system will be an operation control based on in-line particle size measurements using laser diffraction. In industrial practice the operating conditions are often determined by trial and error. To avoid off-spec material the mill is often tuned to lower risk. This results in a relatively large amount of overground material. With respect to the control strategy, the first step was real time particle size monitoring to explore the operating ranges of the jet mill. The controlability is studied in relation to several process inputs and process configurations. A dynamic model of the closed loop grinding plant is developed. particle transport and size reduction inside the mill show a stochastic behaviour and are described by size and state dependent probability functions. Separate experiments are carried out to derive equations for the breakage kinetics of different materials. Numerical flaw simulations are carried out to provide statistic data about the frequency and intensity of collisions between particles in relation to state conditions in the mill. A glass bottom plate will be placed on the mill to observe flow patterns. The influence of several process input variables on the dynamics of the grinding plant and the final product are simulated. Pilot plant experiments are carried out to verify and optimize the dynamic model by direct measurement of the PSD under actual system conditions. The ultimate objective of the dynamic model will be the implementation in a control system. The required setpoints of the mill/classifier system are predicted to obtain the desired product quality at minimum energy use. Furthermore the model can be used for scale up and plant design.