Coupled equilibria play important roles in controlling information flow in biochemical systems, including allosteric molecules and multidomain proteins. In the simplest case, two equilibria are coupled to produce four interconverting states. In this study, we assessed the feasibility of determining the degree of coupling between two equilibria in a four-state system via relaxation dispersion measurements. A major bottleneck in this effort is the lack of efficient approaches to data analysis. To this end, we designed a strategy to efficiently evaluate the smoothness of the target function surface (TFS). Using this approach, we found that the TFS is very rough when fitting benchmark CPMG data to all adjustable variables of the four-state equilibria. After constraining a portion of the adjustable variables, which can often be achieved through independent biochemical manipulation of the system, the smoothness of TFS improves dramatically, although it is still insufficient to pinpoint the solution. The four-state equilibria can be finally solved with further incorporation of independent chemical shift information that is readily available. We also used Monte Carlo simulations to evaluate how well each adjustable parameter can be determined in a large kinetic and thermodynamic parameter space and how much improvement can be achieved in defining the parameters through additional measurements. The results show that in favorable conditions the combination of relaxation dispersion and biochemical manipulation allow the four-state equilibrium to be parameterized, and thus coupling strength between two processes to be determined.