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Run-of-mine ore is usually too large to be useful for construction or metallurgy. Large particles must be reduced to specific sizes to either comply with aggregate sizing regulations, or facilitate mineral liberation; therefore, in the aggregates and mineral processing industries, run-of-mine ore is broken in crushing circuits by cone crushers. Here, we review the cone crusher literature, focusing on the modeling and control of crushing circuits. A total of 61 works published in the primary literature, ranging from 1972 to 2020, are classified and discussed with respect to the model formulation—i.e., population balance models, empirical models, or data-driven models—and control strategy—i.e., proportional-integral-derivative, model predictive control, expert system, or model-free control. The data are summarized in a table that makes locating a particular formulation or strategy quick and easy. The discussion of the current state of the art of crushing circuit modeling and control technologies consolidates the results available in the literature, as well as the challenges that we must overcome to increase crushing performance through control and optimization. The discussion of future trends brings attention to the discrete element method, model based control, plant-wide and mine-to-mill optimization, and machine learning applications for data acquisition and process optimization., Validerad;2021;Nivå 2;2021-06-28 (beamah);Forskningsfinansiärer: Conselho Nacional de Desenvolvimento Científico e Tecnológico (Grant Nos. 402759/2018-4 and 444425/2018-7); Instituto Tecnológico Vale, Ouro Preto, Brazil; SAG Conference Award Foundation