In this paper, we report on the recognition accuracy of the multiframe GMM-based block quantiser for the coding of MFCC features in a distributed speech recognition framework under varying noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For comparison, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion efficiency of the quantiser is a factor in determining the level of recognition accuracy at low to medium levels of additive noise. For high levels of additive noise, the influence of rate-distortion efficiency diminishes and the recognition accuracy becomes dependent on the recognition features.