The accuracy of automated face-recognition systems can and should be tested using experiments that take into account the variables that affect its reliability. These variables include age, sex, ancestry, height, and weight, as well as features unique to a particular individual. In addition, there are image-related issues that are important, such as pose, lighting, and image quality. To aid in this work, the MORPH data corpus was created. The MORPH Database has assembled an array of facial images designed to ensure a large sample size of the facial images of both men and women of different ancestry groups. As many additional images as could be obtained for each individual in the database were sought in an effort to ensure coverage of adult age-related facial changes, ideally from adolescence to old age. With a sizeable and representative database of facial images MORPH began testing the performance of a standard principal component analysis face-recognition (PCA-FR) algorithm in evaluating accuracy rates in terms of correct matches between gallery images (youngest-age images) and probe images (varying older age images). Results from these early expeirments, which are presented in this paper, inform the ways in which normal adult craniofacial age changes might influence automated face-recognition accuracy rates. Understanding the effects of natural craniofacial morphological changes that occur over the adult lifespan is critical to improving automated face-recognition technology that can be used widely in the forensic science community. 10 tables and 17 references