Moving horizon estimation alleviates the computational burden of solving a full information estimation problem by considering a finite horizon of the measurement data, however, it is non-trivial to determine the arrival cost. A commonly used approach for computing the arrival cost is to use a first-order Taylor series approximation of the nonlinear model and then apply an extended Kalman filter. In this paper, an approach to compute the arrival cost for moving horizon estimation based on an unscented Kalman filter is proposed. The performance of such a moving horizon estimator is compared with the one based on an extended Kalman filter and illustrated in a case study. (C) 2008 Elsevier Ltd. All rights reserved.