Reliable estimation of process variables for plant monitoring and control is an important topic that has been studied extensively. The Kalman filter has often been used and has acquired an enviable reputation. However, use of the Kalman filter suffers from two restrictive conditions: (1) it requires state-space models and (2) it has to be tuned online to achieve its best performance. Recently, an alternative methodology based on dynamic data reconciliation has been proposed to overcome the first restriction. Although the approach of dynamic data reconciliation can incorporate any form of model, it involves online optimization that may require long computation time for complex systems. This report explores a new methodology based on a combination of autoassociative neural networks (AANNs) and dynamic data reconciliation that overcomes the need for online tuning, as required by the Kalman filter, as well as online optimization as required by conventional dynamic data reconciliation methods. Simulation examples of a distillation column demonstrate that the AANN-based dynamic data reconciliation approach is capable of effectively attenuating measurement noise and is robust to changes of the noise level in plant measurements and the loss of measurements. (c) 2007 American Institute of Chemical Engineers.