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Adapting acoustic models jointly to both speaker and environment has been shown to be effective. In many realistic scenarios, however, either the speaker or environment at test time might be unknown, or there may be insufficient data to learn a joint transform. Generating independent speaker and environment transforms improves the match of an acoustic model to unseen combinations. Using i-vectors, we demonstrate that it is possible to factorise speaker or environment information using multi-condition training with neural networks. Specifically, we extract bottleneck features from networks trained to classify either speakers or environments. We perform experiments on the Wall Street Journal corpus combined with environment noise from the Diverse Environments Multichannel Acoustic Noise Database. Using the factorised i-vectors we show improvements in word error rates on perturbed versions of the eval92 and dev93 test sets, both when one factor is missing and when the factors are seen but not in the desired combination.