This paper provides a review of the available tuning guidelines for model predictive control, from theoretical and practical perspectives. It covers both popular dynamic matrix control and generalized predictive control implementations, along with the more general state-space representation of model predictive control and other more specialized types, such as max-plus-linear model predictive control. Additionally, a section on state estimation and Kalman filtering is included along with auto (self) tuning. Tuning methods covered range from equations derived from simulation/approximation of the process dynamics to bounds on the region of acceptable tuning parameter values.