The diagnosis of early stage dementia is a highly complex process involving not only a somatic examination but also a neuropsychological assessment of the patient's cognitive capability. The American 'Consortium to Establish a Registry for Alzheimer's Disease' (CERAD) has proposed a set of tests in English which has been translated into German. This paper presents the statistical methodology applied to determine normal ranges adjusted for demographic variables for the German CERAD neuropsychological assessment battery (CERAD-NAB). The study population consists of participants of the Basel Study on the Elderly (Project BASEL) which aims at identifying preclinical markers of Alzheimer's disease. The normative sample has been defined by carefully excluding potentially relevant medical history and concomitant diseases and consists of 617 participants which are between 53 and 92 years old. Test results should be adjusted for gender, age, and years of education. For this purpose, a set of linear models including these predictors and subsets of their interactions and squares was evaluated for all 11 test scores derived from the CERAD-NAB battery. Model selection was based on the PRESS (predicted residual sum of squares) statistic. Although a strict application of this criterion selected 6 different models, a slight compromise allowed to fit all test scores by two models. In several tests of the CERAD-NAB many participants achieve maximal scores. Residuals of such test scores are heavily skewed. An arcsine transformation has been tuned to the data, so that residuals are close to a normal distribution, at least for residuals in the lower quartile which is relevant in diagnosing cognitive impairment. Test results are finally presented as z-scores which can be easily compared to a standard normal distribution. The evaluation of the CERAD-NAB is implemented on the Internet and in an Excel application.