A two-dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5-aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self-organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM-based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. (c) 2005 Society of Chemical Industry.