Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in the faces of upset. Distillation column is generally subjected to disturbances in the feed and the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Secondary measurements are used to adjust the values of the manipulated variables, as the controlled variables are not easily measured or not economically viable to measure (inferential control). In the present paper, an artificial neural network (ANN) based estimator to estimate composition of the distillate is proposed. Nowadays with the advent of digital computers, the demand of the time is to amalgamate the control of various variables to achieve the best results in optimum time. It is therefore required to monitor all the desired variables and perform the control action (feed forward, feed back and inferential) as per algorithm adopted. The developed estimator is tested and the results are compared. The comparison shows that the predictions made by the neural network are in good agreement with results of simulation. (c) 2004 Elsevier B.V. All rights reserved.