In this paper we present a methodology for fuzzy sensor fusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1) It aggregates redundant sensor information; this allows making decision which sensors should be considered for propagation of sensor information. 2) It filters out noise and sensor failure from measurements; this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading form validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.