A key factor for the further distribution of biomass boilers in modern energy systems is the capability of changing the applied feedstock during normal plant operation. This is only possible with the application of advanced control strategies that utilize knowledge about the state variables and varying fuel properties. However, neither the state variables nor the fuel properties are measurable during plant operation and, thus, need to be estimated. This contribution presents a method for the simultaneous real-time estimation of the state variables and the fuel properties in fixed-bed biomass boilers which is a novel approach in the field of biomass boilers. The method bases on an Extended Kalman Filter using a nonlinear dynamic model and measurement data from the combustion process. The estimated variables are the masses of dry fuel and water in the fuel bed as well as the fuel’s bulk density, water content, chemical composition and lower heating value. The proposed method is easy to implement and requires moderate computational effort which increases the potential of its application at actual biomass boilers. The proposed method is verified with simulation studies and by test runs performed at a representative small-scale fixed-bed biomass boiler. The estimation results show a good agreement with the actual values, demonstrating that the proposed method is capable of accurately estimating the biomass boiler’s state variables and simultaneously its fuel properties. For this reason, the presented method is a key technology to ensure the further distribution of biomass boilers in modern energy systems.