While the corpus-based research relies on human annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. Detection of errors in annotated corpora is important for corpus-based natural language processing. In this paper, we propose a method to detect errors in corpora using support vector machines (SVMs). This method is based on the idea of extracting exceptional elements that violate consistency. We propose a method of using SVMs to assign a weight to each element and to find errors in a POS tagged corpus. We apply the method to English and Japanese POS-tagged corpora and achieve high precision in detecting errors.