Estimating the cycle time of every job in a semiconductor manufacturing factory is a critical task to the factory. Many recent studies have shown that pre-classifying a job before estimating the cycle time of the job was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, an artificial neural network (ANN) approach that equally divides and post-classifies jobs is proposed in this study in which a job is post-classified by a BPN instead after the forecasting error is generated. In this novel way, only jobs which cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, some data were collected from an actual semiconductor manufacturing factory. According to experimental results, the forecasting accuracy (measured with root mean squared error (RMSE)) of the proposed methodology was significantly better than those of the other approaches in most cases by achieving a 16%~44% (and an average of 29%) reduction in RMSE over the comparison basis – multiple-factor linear combination (MFLC). The effect of post-classification was also evident.