In a modern chemical plant, distributed control systems provide access to a wide variety of process data that typically contain both random and gross errors. Although data reconciliation has been an often-studied topic to reduce the amount of error in measurements, applications of dynamic data reconciliation to problems in industry are virtually nonexistent. This is because standard formulations of the dynamic data reconciliation problem result in large nonlinear programs, which are thought to be too difficult to solve in real time. With increases in computing speeds and improvements in optimization technology, these concerns are diminishing. This paper describes the implementation of a dynamic data reconciliation application at an ExxonMobil Chemical Company plant. The specifics of the application will be discussed and some solutions to the practical problems encountered in an industrial setting are presented.