Predicting box office receipts of a particular movie has intrigued many researchers, domain experts and industry leaders as a challenging problem. In this paper, we report on the current status of a prediction system being built at the Institute for Research in Information Systems (IRIS) at Oklahoma State University since 1998. In our model, the forecasting problem is converted into a classification problem, that is, rather than forecasting the pinpoint estimate of box office receipts, a movie is classified into one of nine financial success categories, ranging from a "flop" to a "blockbuster." The prediction results of different datasets representing different time windows and different combination of predictors are presented using average percent hit rate of bingo and oneaway predictions. In the latest tests the prediction results of artificial neural networks improved to almost 50% on "bingo" and close to 90% on "one-away".