The sieve-tray distillation column mass-transfer efficiency was successfully modeled using a neural network. The database developed by Garcia and Fair (Ind. Eng. Chem. Res. 2000, 39, 1809) was utilized to train and validate the neural network model. The results indicate that, if the system is similar to the data used to train the neural network, the purely empirical neural network model yields very accurate predictions. However, in areas where data are lacking, even if the input parameters are in the same range as those in the database, the neural network model must make extrapolations and will therefore make unreliable predictions. MS Excel programs based on the results from neural networks were developed for the prediction of the sieve-tray efficiency and structured packing height equivalent to a theoretical plate. These programs represent a convenient and easy to use tool. Moreover, they make better predictions than a single neural network and make it possible to detect extrapolations and misleading predictions.