In state estimation problems, often, the true states satisfy certain constraints resulting from the physics of the problem, that need to be incorporated and satisfied during the estimation procedure. Amongst various constrained nonlinear state estimation algorithms proposed in the literature, the unscented recursive nonlinear dynamic data reconciliation (URNDDR) presented in [1] seems to be promising since it is able to incorporate constraints while maintaining the recursive nature of estimation. In this article, we propose a modified URNDDR algorithm that gives superior performance when compared with the basic URNDDR. The improvements are obtained via better constraint handling and are demonstrated on representative case studies [2,3]. In addition to this modification, an efficient strategy combining basic unscented Kalman filter (UKF), URNDDR and modified URNDDR is also proposed in this article for solving large scale state estimation problems at relatively low computational cost. The utility of the proposed strategy is demonstrated by applying it to the Tennessee Eastman challenge process [4]. (C) 2010 Elsevier Ltd. All rights reserved.