Data stored on disks and tapes or transferred over communications links in commercial computer systems generally contains significant redundancy. A mechanism or procedure which recodes the data to lessen the redundancy could possibly double or triple the effective data densitites in stored or communicated data. Moreover, if compression is automatic, it can also aid in the rise of software development costs. A transparent compression mechanism could permit the use of"sloppy" data structures, in that empty space or sparse encoding of data would not greatly expand the use of storage space or transfertime; however, that requires a good compression procedure. Several problems encountered when common compression methods are integrated into computer systems have prevented the widespread use of automatic data compression. For example (1) poor runtime execution speeds interfere in the attainment of very high data rates;(2) most compression techniques are not flexible enough to process different types of redundancy;(3) blocks of compressed data that have unpredictable lengths present storage space management problems. Each compression strategy poses a different set of these problems and, consequently, the use of each strategy is restricted to applications where its inherent weaknesses present no critical problems. This article introduces a new compression algorithm that is based on principles not found in existing commercial methods. This algorithm avoids many of the problems associated with older methods in that it dynamically adapts to the redundancy characteristics of the data being compressed.