It is not uncommon for cochlear implant patients to have individual electrodes that produce anomalous percepts that impair or prevent the effective transmission of auditory information. Exhaustive psychophysical testing to detect all such information channels is time prohibitive; however, if impaired channels could be identified quickly then the application of remediation strategies becomes more cost-effective. Previous studies have suggested that the missing speech information could produce a predictable pattern of errors in speech-token identification tasks [1, 2]. This study investigates the application of partial least squares discriminant analysis to identifying the presence of channel-specific impairments based on confusion matrices generated from vowel and consonant token identification tasks. The results of this study, using normal-hearing subjects tested with acoustic models, suggest that the partial least squares discriminant analysis can successfully distinguish impaired and unimpaired models, as well as identify channel-specific impairments, without requiring a significant amount of labeled training data. ©2010 IEEE.