A neural network-based model of the burden layer thickness in the blast furnace is presented. The model is based on layer thicknesses estimates from a single radar measurement of the burden (stock) level in the furnace and describes the dependence between the layer thickness and key charging variables. An evolutionary algorithm is applied to train the network weights and connectivity by optimizing the model structure and parameters simultaneously, tackling part of the parameter estimation by linear least squares. This enhances convergence and results in parsimonious and transparent network models with actions that can be explained. Finally, the networks are used in a hybrid model for analyzing novel charging programs and for studying the limits of the charging process.