Cochlear implant (CI) recipients have difficulty understanding speech in noise even at moderate signal-to-noise ratios. Knowing the mechanisms they use to understand speech in noise may facilitate the search for better speech processing algorithms. In the present study, a computational model is used to assess whether CI users' vowel identification in noise can be explained by formant frequency cues (F1 and F2). Vowel identification was tested with 12 unilateral CI users in quiet and in noise. Formant cues were measured from vowels in each condition, specific to each subject's speech processor. Noise distorted the location of vowels in the F2 vs F1 plane in comparison to quiet. The best fit model to subjects' data in quiet produced model predictions in noise that were within 8% of actual scores on average. Predictions in noise were much better when assuming that subjects used a priori knowledge regarding how formant information is degraded in noise (experiment 1). However, the model's best fit to subjects' confusion matrices in noise was worse than in quiet, suggesting that CI users utilize formant cues to identify vowels in noise, but to a different extent than how they identify vowels in quiet (experiment 2).