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This paper presents a new microphone-array post-filtering algorithm for distant speech recognition (DSR). Conventionally, post-filtering methods assume static noise field models, and using this assumption, employ a Wiener filter mechanism for estimating the noise parameters. In contrast to this, we show how we can build the Wiener post-filter based on actual noise observations without any noise-field assumption. The algorithm is framed within a state-of-the-art beamforming technique, namely maximum negentropy (MN) beamforming with super directivity. We investigate the effectiveness of the proposed post-filter on DSR through experiments on noisy data collected in a car under different acoustic conditions. Experiments show that the new post-filtering mechanism is able to achieve up to 20% relative reduction of word error rates (WER) under the represented noise conditions, as compared to a single distant microphone. In contrast, super-directive (SD) beamforming followed by Zelinski post-filtering achieves a relative WER reduction of only up to 11%. Other post-filters evaluated perform similarly in comparison to the proposed post-filter.