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A non-linear discriminant analysis based approach to feature space dimensionality reduction in noise robust automatic speech recognition (ASR) is proposed. It utilizes a correlation based distance measure instead of the conventional Euclidean distance. The use of this "correlation preserving discriminant analysis" (CPDA) procedure is motivated by evidence suggesting that correlation based cepstrum distance measures can be more robust than Euclidean based distances when speech is corrupted by noise. The performance of CPDA is evaluated in terms of the word error rate obtained by using CPDA derived features on the Aurora 2 speech in noise corpus, and is compared to the commonly used linear discriminant analysis (LDA) approach to feature space transformations.