Abstract Although on‐line measurements play a vital role in process control and monitoring process performance, they are corrupted by noise and occasional outliers (such as noise spikes). Thus, there is a need to rectify the data by removing outliers and reducing noise effects. Well‐known techniques such as Kalman Filtering have been used effectively to filter noise measurements, but it is not designed to automatically remove outliers. A new methodology based on the Kalman filter rectifies noise as well as outliers in measurements. Filter equations were formulated in the form of probability distributions. Then the Expectation‐Maximization algorithm was used to find the maximum‐likelihood estimates of the true measurement values based on a state‐space model, past data, and current observations. This approach was evaluated when the assumption of normally distributed outliers is not valid. The method can be used with any dynamic process model, as shown by integrating it with an extended Kalman Filter and by an augmented linear state‐space model to account for unmeasured disturbances. It also can be used to provide diagnostic information about changes to the process or sensor failures.