This paper presents the application of self-optimizing concepts for more efficient generation of steady-state surrogate models. Surrogate model generation generally has problems with a large number of independent variables resulting in a large sampling space. If the surrogate model is to be used for optimization, utilizing self-optimizing variables allows to map a close-to-optimal response surface, which reduces the model complexity. In particular, the mapped surface becomes much "flatter", allowing for a simpler representation, for example, a linear map or neglecting the dependency of certain variables completely. The proposed method is studied using an ammonia reactor which for some disturbances shows limit-cycle behaviour and/or reactor extinction. Using self-optimizing variables, it is possible to reduce the number of manipulated variables by three and map a response surface close to the optimal response surface. With the original variables, the response surface would include also regions in which the reactor is extinct. (C) 2018 Elsevier Ltd. All rights reserved.