Progress curve experiments combined with optimal experimental design (OED) are an efficient approach to determine enzyme kinetics. However, it is hardly possible to verify why specific experiments are suggested for nonlinear enzyme kinetic model identification. Therefore, we systematically investigated the surface and contour plots of the sensitivities and of the OED criteria which are based on sensitivities. The model reaction was an enzyme catalyzed self-ligation of aldehydes to chiral 2-hydroxyketones. The visualization improved the understanding of OED and allowed for deducing and confirming five suggestions for kinetic identification: (1) Avoid experiments vicinal to the reaction equilibrium, (2) Choose the design space as large as possible, (3) Prefer D(eterminant)- and E(igenvalue)-criteria over the A(verage)-criterion, (4) Apply enzyme concentrations such that the reaction does not complete too fast, and (5) Few optimal experiments result in significantly improved parameter estimations. The graphical analysis also provides information about selecting appropriate optimization algorithms. (C) 2017 American Institute of Chemical Engineers.