CC4 (the 4th version of corner classification) neural network is a new type of corner classification training algorithm for three-layered feedforward neural networks. It has been provided as a document classification approach for metasearch engine Anvish. On the condition that documents are almost of the same size, CC4 neural network is an effective document classification algorithm. However, when there is great difference in document sizes, CC4 neural network does not perform well. This paper aims to extend the original CC4 neural network for effectively classifying documents having much difference in sizes. To achieve this goal, the authors propose a MDS-NN based data indexing method thus making all documents be mapped to k-dimensional points while their distance information is kept well. The authors also extend CC4 neural network so that it can accept k-dimensional indexes of documents as its input, then transform these indexes to binary sequences required by CC4 neural network. The experimental results show that the performance of ExtendedCC4 is much better than that of InitialCC4 when there is a great difference in document sizes. At the same time, the high classification precision of ExtendedCC4 has much relationship with the effectiveness of indexing methods.