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In this paper a reference model weighting (RMW) method is proposed for fast hidden Markov model (HMM) adaptation which aims to use only one input test utterance to online estimate the characteristic of the unknown test noisy environment. The idea of RMW is to first collect a set of reference HMMs in the training phase to represent the space of noisy environments, and then synthesize a suitable HMM for the unknown test noisy environment by interpolating the set of reference HMMs. Noisy environment mismatch can hence be efficiently compensated. The proposed method was evaluated on the multi-condition training task of Aurora2 corpus. Experimental results showed that the proposed RMW approach outperformed both the histogram equalization (HEQ) method and the distributed speech recognition (DSR) standard ES 202 212 proposed by European Telecommunications Standards Institute (ETSI).