To find an easy-to-use, automated tool to identify technical vocabulary applicable to learners at various levels, nine statistical measures were applied to the 7.3-million-word "commerce and finance" component of the British National Corpus. The resulting word lists showed that each statistical measure extracted a different level of specialized vocabulary as measured by word length, vocabulary level, US native speaker grade level, and Japanese school textbook vocabulary coverage, and that these measures produced level-specific words; i.e., beginning-level basic business words were identified using "Cosine" and the "complimentary similarity measure"; intermediate-level business words were extracted using "log-likelihood", the "chi-square test", and the "chi-square test with Yates's correction"; and advanced-level business word lists were created using "mutual information" and "McNemar's test". We conclude that these statistical measures are effective tools for identifying multi-level specialized vocabulary for pedagogical purposes. (Contains 2 figures and 4 tables.)