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The ability to screen a large population and identify symptoms of Mild Cognitive Impairment (MCI), the earliest stage of dementia, is becoming increasingly important as the aged population grows and research gains are made in delaying the progression of cognitive degeneration. In this paper we present an end-to-end system for automatically scoring spoken responses to a narrative recall test commonly administered to seniors as part of clinical neuropsychological assessment. In this test, a patient listens to a brief narrative, immediately retells it, then retells it again later in the session, after some time has elapsed. ASR transcripts of retellings are automatically aligned to the source narrative, and features are extracted that replicate the published clinical scoring method, which are then used for automatic assessment using a classifier. On a test corpus of 72 subjects, we empirically evaluate different ASR adaptation strategies and analyze the errors and their relationship to clinical assessment accuracy. Despite imperfect recognition, the system presented here yields classification accuracy comparable to that of scores derived from manual transcripts. Our results show that automatic scoring of neuropsychological assessment such as Wechsler Logical Memory (WLM) is practical for screening large cohorts.