Six different modelling techniques were considered for the recombinant Escherichia coli fermentation process. These are Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Auto-Regressive Moving Average with eXogeneous inputs (ARMAX), Non-linear ARMAX (NARMAX) and Artificial Neural Networks. The models use industrial on-line data from the process as input variables in order to forecast the concentrations of biomass and recombinant protein normally only available from off-line laboratory analysis. The models’ performances are compared by prediction error and graphical fit using results obtained from a common testing set of fermentation data.