The statistical approach to the analysis of document collections and retrieval therefrom has proceeded along two main lines, associative machine searching and automatic classification.The former approach has been favored because of the tendency of people ia the computer field to strive for new methods of dealing with the literature--methods which do not resemble those of traditional libraries.But automatic classification study also has been thriw ing; some of the reasons for this are discussed.The crucial question of the quality of automatic classification is treated at considerable length, and empirical data are introduced to support the hypothesis that classification quality improves as more information about each document is used for input to the classification program.Six nonjudgmental criteria are used in testing the hypothesis for 100 keyword lists (each list representing a document) for a series of computer runs in which the number of words pet" document is increased progressively from 12 to 36.Four of the six criteria indicate the hypothesis holds, and two point to no effect.Previous work of this kind has been confined to the range t~f one through eight words per document.Finally, the future of automatic classification and some of the practical problems to be faced are outlined.

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