The robustness of the ETSI-Aurora standardized feature extraction schemes is investigated for phoneme based recognition tasks of German speech data. The recognition tasks are an isolated command word recognition and the recognition of connected digits. The motivation of this work is the easy extensibility of a whole word recognition system by allowing also the recognition of phoneme based word HMMs. The recognition performance has been determined for different numbers of HMM states and different numbers of Gaussians per state. It turns out that fairly high recognition rates can be achieved also for noisy data when applying the second robust ETSI frontend.