This paper describes an extension to an automatic speech recognition system that improves the robustness concerning varying environments. A dedicated control unit tries to derive an optimal set of parameters for a Wiener Filter based noise reduction unit aiming at maximum recognition performances in different environments. The input measure for the control unit is derived from the speech signal. Apart from the SNR level, several other measures are investigated. The controlled parameters are closely related to the strength of the noise reduction. Several non-linear methods such as the Tabulated References and Neural Networks serve as the core of the control unit. Experiments on realistic handsfree as well as non-handsfree speech data show that the word error rate can be reduced by as much as 31% through the proposed methods. An already optimized static configuration of the applied noise reduction hereby serves as the baseline level.