Studying the performance of multisensor tracking systems against maneuvering targets involves Monte Carlo simulations with the tracking algorithms implemented in a sophisticated computer simulation of the multisensor system. However, a simplified method for predicting the performance of a multisensor tracking system against maneuvering targets is needed for confirmation of the computer simulations, real-time command and control decisions such as multisensor resource allocation, and systems engineering of complex multisensor systems. The challenge of accurate performance prediction arises from the lack of covariance consistency of the Kalman filter when tracking maneuvering targets. In this paper, a method for performance prediction of a nearly constant velocity Kalman filter is extended to tracking a maneuvering target with multiple dispersed sensors on an oblate earth. Given target position and acceleration as a function of time, the tracking performance of each sensor is expressed as a sensor-noise only (SNO) covariance and maneuver lag or filter bias. In the fusion of the data from the multiple sensors, the SNO covariances fuse for a smaller covariance, while the maneuver lags fuse with a gain proportional to the inverse of the covariances for the sensor tracks. This method can also be used to predict the performance of a multisensor system that include one, two, and/or three dimensional sensors. The results of Monte Carlo simulations of multisensor tracking of a maneuvering tar-get are used to illustrate the accuracy of methods for performance prediction.