An iterative hybrid performance improvement approach integrating artificial neural network modelling and Pareto genetic algorithm optimisation was developed and tested. The optimisation procedure, code named NNREGA, was tested for tuning pilot scale Continuous Variable Discharge Concentrator (CVD) in order to simultaneously maximise recovery and upgrade ratio of gold bearing sulphides from a polymetallic massive sulphide ore. For the tests the CVD was retrofitted during normal operation on the flotation tailings stream. On the basis of mineralogical data showing strong pyrite-gold association in the flotation tailings, iron assays were used as an indicator of CVD performance on recovery of gold bearing sulphides. Initially, 17 pilot scale statistically designed tests were conducted to assess metallurgical performance. The Matlab 2010a software was used to train and simulate back propagation ANNs on experimental results. Regression models developed from simulation data were used to formulate the objective functions used to optimise the CVD using the NSGA-II genetic algorithm. Results show that the NNREGA procedure provides an efficient way of exploring the design space to learn the relationship between interacting variables and outputs and is capable of generating the operating line, which is a non-dominated recovery/grade line. The paper forms a basis for future work aiming to model and scale up processing equipment. (C) 2015 Elsevier B.V. All rights reserved.