Approximately one year's operational data of a TATA Steel blast furnace were subjected to a multiobjective optimisation using genetic algorithms. Data driven models were constructed for productivity, CO(2) content of the top gas and Si content of the hot metal, using an evolutionary neural network that itself evolved through a multiobjective genetic algorithm as a tradeoff between the accuracy of training and the network complexity. The final networks were selected using the corrected Akaike information criterion. Bi-objective optimisation studies were subsequently carried out between the productivity and CO(2) content with various constraints at the Si level in the hot metal. The results indicate that a productivity increase would entail either a compromise of the CO(2) fraction in the top gas or the Si content in the hot metal. The Pareto frontiers presented in this study provide the best possible parameter settings in such a scenario.