With the explosive increase in web based information, the need for an intelligent agent for automatic classification has also been increased resulting in many research discoveries in this area. Machine Learning (ML) based document classification is now the prevalent approach. However, classification by ML may not keep the same performance because the knowledge generated from the training set may not be appropriate for certain types of web information. People are often concerned more about the newly uploaded information such as web based online news than information already available. This explains why it is not widely used in real applications. However, the manual classification method, by the domain users, cannot be a solution either until the knowledge acquisition bottleneck issue is resolved. Multiple Classification Ripple Down Rules, an incremental knowledge acquisition method, is suggested to overcome this problem with fast learning and low cost maintenance.