We explore the use of out-of-domain monolingual data for the improvement of automatic speech recognition (ASR) of code switched speech. This is relevant because annotated code switched speech data is both scarce and very hard to produce, especially when the languages concerned are under-resourced, while monolingual corpora are generally better-resourced. We perform experiments using a recently-introduced small five language corpus of code-switched South African soap opera speech. We consider specifically whether ASR of English– isiZulu code-switched speech can be improved by incorporating monolingual data from unrelated but larger corpora. TDNNBLSTM acoustic models are trained using various configurations of training data. The utility of artificially-generated bilingual English–isiZulu text to augment language model training data is also explored. We find that English-isiZulu speech recognition accuracy can be improved by incorporating monolingual out-of-domain data despite the differences between the soap-opera and monolingual speech.