The aim of this paper was to study the efficiency of sound duration, degree of sound voicing and sound energy in a rejection procedure of an automatic speech recognition system. A modelling of the three parameters was achieved using statistical models estimated on vocabulary words, out-of-vocabulary words and noise tokens. The rejection of out-of-vocabulary words and noises depended on the score obtained by comparing the probability given by the different models. However, such an approach also cause false rejection (rejection of vocabulary words). A trade-off was therefore necessary between the false rejection rate and the false alarm rate on out-of- vocabulary words and noise tokens. The degree of voicing turned out to be the most efficient parameter for rejecting noise tokens; it reduced the HMM false acceptance rate from 6.3% down to 2.3% for the same amount of false rejection rate (9%). The duration parameter provided better performance for laboratory data, reducing the error rate on French numbers from 3.1% to 1.5% for a 5% false rejection rate.