This paper addresses the problem of how to identify and reject Out-Of-Vocabulary (00V) words and background noises using an isolated word speaker independent Hidden Markov Model (HMM) recognizer. The proposed method builds a garbage model as a combination of context independent subword HMM units to represent all possible 00V words and background noises. The rejection rate is adjusted by an appropriate penalty that depends on the utterance duration and then is applied to the garbage model acoustic likelihood. This approach makes the behavior of the system independent of the utterance duration. Additionally, we provide linguistic information to the garbage model so that illegal phone sequences are not allowed. This approach reduces the average branching factor of the garbage model and increases the rejection of the OOV words.