This paper presents a cross-lingual projection technique for training class-based language models. We borrow from previous success in projecting POS tags and NER mentions to that of a trained classbased language model. We use a CRF to train a model to predict when a sequence of words is a member of a given class and use this to label our language model training data. We show that we can successfully project the contextual cues for these classes across pairs of languages and retain a high quality class model in languages with no supervised class data. We present empirical results that show the quality of the projected models as well as their effect on the down-stream speech recognition objective. We are able to achieve over half the reduction of WER when using the projected class models as compared to models trained on human annotations.