Large-scale systems are formed by the interconnection of several subsystems, whose different spatial and temporal characteristics make them significantly heterogeneous. The optimal management of such systems must generally deal not only with issues related to large dimensionality and strong nonlinearity, but also with the presence of several interactions between the subsystems, which have a significant influence on the local control decisions and the overall system optimality. For such large-scale systems, model predictive control (MPC) is an attractive control strategy and can be implemented in centralized or decentralized configurations. It has been shown that, to achieve a flexible and reliable control structure with optimum overall system performance, individual decentralized controllers have to be coordinated and driven toward the performance of a centralized controller. In this work, three coordination strategies that have been reported in the literature, viz., communication based coordination, cooperation based coordination, and price driven coordination, are evaluated for controlling multivariable processes. These three strategies have been evaluated on a benchmark chemical engineering system and on a quadruple tank system (via simulations), on the basis of their robustness, stability, and performance in comparison to that of a centralized MPC implementation. The ability to deal with a variety of model uncertainties and the coordination between the controllers within and across a hierarchy are some important aspects that have been investigated.