Automatic language identification has been applied to short texts such as queries in information retrieval, but it has not yet been applied to metadata records. Applying this technology to metadata records, particularly their title elements, would enable creators of metadata records to obtain a value for the language element, which is often left blank due to a lack of linguistic expertise. It would also enable the addition of the language value to existing metadata records that currently lack a language value. Titles lend themselves to the problem of language identification mainly due to their shortness, a factor which increases the difficulty of accurately identifying a language. This study implemented four proven approaches to language identification as well as one open-source approach on a collection of multilingual titles of books and movies. Of the five approaches considered, a reduced N-gram frequency profile and distance measure approach outperformed all others, accurately identifying over 83% of all titles in the collection. Future plans are to offer this technology to curators of digital collections for use.