This paper presents an approach to robust speech recognition based on neighborhood space, which can achieve performance robustness under mismatch between training and testing conditions. This approach uses neighborhood space of each underlying model to produce Bayesian predictive density as observation probability density. Experimental results show that the proposed method improves the performance robustness.