Results are reported from a dynamic modelling study conducted on a pilot scale anaerobic fluidized bed. The objective of the study was to generate data to identify, evaluate and eventually calibrate a dynamic model for on-line estimation and forecasting in a process control system. Experiments consisted of pulse inputs of glucose, propionic acid, and acetic acid during the treatment of a distillery wastewater. The dynamic response of the methane and carbon dioxide production rates, hydrogen content of the biogas, effluent volatile acid concentrations and effluent COD concentrations are shown. A model structure was postulated based on the results observed in the dynamic experiments. An extended Kalman filter state estimation algorithm was employed to provide estimates of unmeasured process states and parameters. The use of the state estimation technique improved the performance of the model, helped locate model inadequacies, and provided information to direct further model development.