The paper deals with the development of acoustic models of foreign words for a German speech recognizer. The recognition quality of foreign words is crucial for the overall performance of a system in application fields like spoken dialogue systems, when foreign words occur as proper names. One of the main problems in the modeling of foreign words is the limitation of training data, which must contain samples of the non-native pronunciation of the foreign sounds. In order to obtain robust acoustic models, which are still precise enough, we compare several methods to map or to merge the models of phonemes, which are pronounced in a similar way by German speakers. We utilize an entropy-based distance measure between sets of phoneme models. The best approach yields a reduction of 16.5 % word error rate, when compared to a baseline system.