Narrative, spontaneous speech can provide a valuable source of information about an individual’s cognitive state. Unfortunately, clinical transcription of this type of data is typically done by hand, which is prohibitively time-consuming. In order to automate the entire process, we optimize automatic speech recognition (ASR) for participants with Alzheimer’s disease (AD) in a relatively large clinical database. We extract text features from the resulting transcripts and use these features to identify AD with an SVM classifier. While the accuracy of automatic assessment decreases with increased WER, this is weakly correlated (-0.31). This relative robustness to ASR error is aided by selecting features that are resilient to ASR error.