A segment-based approach to automatic language identification is discussed which is based on the idea that the acoustic structure of languages can be estimated by segmenting speech into broad phonetic categories. Automatic language identification can then be achieved by computing features that describe the phonetic and prosodic characteristics of the language, and using these feature measurements to train a classifier to distinguish between languages. As a first step in this approach, a multilanguage neural-network-based segmentation and broad classification algorithm using seven broad phonetic categories has been built. The algorithm was trained and tested on separate sets of speakers of American English, Japanese, Mandarin Chinese, and Tamil. It currently performs with an accuracy of 82.3% on the utterances of the test set.<<ETX>>