This paper describes a novel technique to cluster Out-Of-Vocabulary (OOV) word tokens in a LVCSR system used for transcribing broadcast news speech data. The system is composed of two blocks: (1) an OOV word detector and (2) a clustering module working on the detected OOV word segments. This combination allows a more reliable detection of repeated OOV words than would be possible with the OOV detector only. In the paper we focus our attention on the second part of the system i.e. the cluster-ing algorithm. This algorithm is based on the estimation of the entropy. The proposed algorithm gives better per-formance than a classical incremental clustering algorithm based on a distance threshold.