Part-of-Speech tagging, the assignment of Parts-of-Speech to the words in a given context of use, is a basic technique in many systems that handle natural languages. This paper describes a method for supervised training of a Part-of-Speech tagger using a committee of Support Vector Machines on a large corpus of annotated transcriptions of spoken Dutch. Special attention is paid to the decomposition of the large data set into parts for common, uncommon and unknown words. This does not only solve the space problems caused by the amount of data, it also improves the tagging time. The performance of the resulting tagger in terms of accuracy is 97.54 %, which is quite good, where the speed of the tagger is reasonably good.