When a speech recognition system is deployed in the real world, environmental interference will make noisy speech signals and reference models mismatched and cause serious degradation in recognition accuracy. To deal with the effect of environmental mismatch, a family of signal limiters has been successfully applied to a template-based DTW recognizer to reduce the variability of speech features in noisy conditions. Though simulation results indicate that heavily smoothing can effectively reduce the variability of speech features in low signal-to-noise ratio (SNR), it would also cause the loss of information in speech features. Therefore, we suggest that the smoothing factor of a signal limiter should be related to SNR and adapted on a frame by frame basis. In this paper, an adaptive signal limiter (ASL) is proposed to smooth the instantaneous and dynamic spectral features of reference models and test speech. By smoothing spectral features, the smoothed covariance matrices of reference models can be obtained by means of maximum likelihood (ML) estimation. A speech recognition task for multispeaker isolated Mandarin digits has been conducted to evaluate the effectiveness and robustness of the proposed method. Experimental results indicate that the adaptive signal limiter can achieve significant improvement in noisy conditions and is more robust than the hard limiter over a wider range of SNR values.