The effect of adding noise to an expression-induction model of language evolution was investigated. The model consisted of a number of artificial people who were able to infer the denotation of basic colour terms from examples of colours which the words had been used to identify, using a Bayesian inference procedure. The artificial people would express colours to one-another, so producing data from which other people could learn. Occasionally they would be creative, which allowed new words to enter the language. When certain points in the colour space were made especially salient, so that the artificial people were more likely to remember colours at these points, the languages emerging over a number of generations in evolutionary simulations replicated the typological patterns seen in the 110 languages of the world colour survey. It was found that if random noise was added to the data from which the artificial people learned, this had no major effect on the emergent languages, demonstrating that the Bayesian inference procedure is able to learn effectively despite the presence of random noise, even when placed in an evolutionary context