This paper describes the performance of the combination of Multi-Environment Model-based LInear Normalization, MEMLIN, which provides an estimation of the uncorrupted feature vector, with Maximum Likelihood Linear Regression, MLLR, for the collected database under the auspices of the IST-EU STREP project HIWIRE. In this work the results for the non-native adaptation task (NNA) are presented. The HIWIRE project database consist on command and control aeronautics application utterances pronounced by non-native speakers which are digitally corrupted with airplane cockpit noise. Thus, three noise conditions are defined: low, medium and high noise. In the proposed system, each MEMLIN-normalized feature vector is decoded using the MLLR-adapted acoustic models. The experiments show that an important improvement is reached combining MEMLIN and MLLR methods for all kinds of non-native speakers and noise conditions.