How well can human faces be identified by humans and by computers, using subjectively judged "feature" descriptions like long ears, wide-set eyes, etc.? Three classes of experiments are reported: 1) Gathering, analysis, and assessment of face-feature data for 255 faces. 2) Computer identification-studies. 3) Human identification-studies. A set of 22 features was evolved from an initially larger set to provide relevant, distinctive, relatively independent measures which can be judged reliably. Computer studies and a mathematical model established limits of performance of a person attempting to isolate a face from a population using feature descriptions. The model predicts that under certain conditions approximately 6 of an individual's features are required to isolate him from a population of 255. Human experiments under similar conditions showed unique identification occurred with an average of about 7 features. The model predicts that for a population of 4×106, only 14 feature-descriptions are required. These studies form a foundation for continuing research on real-time man-machine interaction for computer classification and identification of multidimensional vectors specified by noisy components.