The presence of autocorrelated measurement errors and/or measurement bias in process measurements poses serious problems in the rectification of data taken from dynamic processes. The proposed procedure to resolve these problems involves the use of recurrent neural networks (RNN) and the extended Kalman filter (EKF). By interpreting RNNs within a nonlinear state-space context, a state-augmented EKF can be used to optimally estimate both the states of the RNNs and noise and bias models. RNN models can be identified off-line and utilized for data rectification within the extended Kalman filter in process environments in which badly autocorrelated measurement errors exist in the data. The same technique is also used to estimate measurement bias present in both process input and output variables. This approach has the advantage that models developed from "first principles" are not required and that rectification can be performed solely on the basis of the contaminated dynamic data.