A set of systematic experiments on intelligent email categorization has been conducted with different machine learning algorithms applied to different parts of data in order to achieve the most correct classification. The categorization is based on not only the body but also the header of an email message. The metadata (e.g. sender name, sender organization, etc.) provide additional information that can be exploited to improve the categorization capability. Results of experiments on real email data demonstrate the feasibility of our approach to find the best learning algorithm and the metadata to be used, which is a very significant contribution in email classification. It is also shown that categorization based only on the header information is comparable or superior to that based on all the information in a message for all the learning algorithms considered.