During a fermentation process, variables such as concentrations are determined by off-line laboratory analysis, making this set of variables of limited use for control purposes. However, these variables can be on-line estimated using soft sensors. The objective of this study is to present the state of the art of state estimator techniques. Special attention was given to filtering techniques, namely extended Kalman filter, adaptive observers, and artificial neural networks (ANN). It is shown that software based state estimation is a powerful technique that can be successfully used to enhance automatic control performance of biological systems as well as in system monitoring and on-line optimisation.