In this paper a multi sensor data fusion-based target tracking system is presented. The system includes neurofuzzy multisensor data fusion (MSDF) in order to overcome the limitation of the use of a single sensor. It has the capability of minimising the noise that contaminates the sensor measurements and excludes the faulty (invalid) measurements from use in the estimation process. Despite being a simple algorithm, it can deal with the data fusion problem using noisy non-linear sensors as well as linear sensors. A neuro-tuzzy kinematics process model is also employed in this target tracking system to cope with the lack of a priori knowledge of the target dynamics. Although no a priori statistical knowledge of the target dynamics and the sensors are involved in the estimation process, the performance of the proposed system is comparable with the Extended Kalman Filter-based target tracking system which uses the exactly known process model of the target.