The industrial fed-batch yeast cultivation process has been divided into four different metabolic phases (adaptation, carbon limited, oxygen limited and maturation) by a neuro-fuzzy classification model that consists of 4 applied linguistic rules on 2 state variables: oxygen uptake rate and liquid volume. The membership functions have been automatically adapted by this fuzzy perceptron, i.e., by a supervised learning algorithm initialized by prior operator's knowledge. Process compartmentalization has made easier and more realistic a subsequent state estimation of the biomass concentration with separate artificial neural networks combined with balance equations. Static networks with local recurrent memory structures were used, and the inputs were standard cultivation state variables: respiratory quotient, molasses feed rate, ethanol concentration, etc. This hybrid approach is generally applicable to state estimation or prediction when different sources of process information and knowledge have to be integrated.