A chemical process that involves the interplay between chemical reaction(s) and transport phenomena may be operated in one of several possible operating regimes. In the development of such a process, the operating regime which yields desirable performance at a smaller scale should be identified to help realize successful scale-up of the process. In the past, experimental procedures for identifying operating regimes have been proposed, which utilize the qualitative trends of the response of a process to changes in operating conditions. In this study, an alternative approach which utilizes both experimental data and mathematical modeling is explored. The rationale behind this approach is that an incorrect assumption with regard to an operating regime may result in a false dependency between particular process variables. On the basis of this hypothesis, operating regime determination can be formulated around a dependency test. In this paper, the computation of a dependency measure is based on the mutual information approach as defined in information theory. The methodology proposed is able to handle the situation where mechanistic knowledge such as chemical reaction kinetics is missing. In practice, this information is often not available during the early stages of process development. The proposed methodology is tested and compared with alternative approaches on a simulated toluene nitration process. Its potential in terms of achieving a reliable discrimination between different assumptions with regard to the operating regime with fewer constraints on the experimental requirements is demonstrated.