Abstract Holistic production control is a concept that introduces production optimisation by employing model-based, closed-loop control of the principal production Performance Indicators (pPIs). The concept relies on the development of a simple black-box model that describes the relation between the main pPIs and the most influential input (manipulative) variables. In this article the modelling aspects of the holistic production control implementation are presented. The main steps of the production modelling procedure are described, such as data preprocessing, the definition of pPIs, the selection of input variables and the derivation of black-box models. Particular emphasis is given to a modelling approach based on neural networks and a corresponding modelling assistant tool, which has been developed to support the modelling procedure. The approach is illustrated on the Tennessee Eastman benchmark process, where neural network models for three main production performance indicators, i.e., costs, quality and production rate, are derived.