THIS PAPER INVESTIGATES MACHINE VISION TECHNOLOGIES FOR QUANTITATIVELY MEASURING COMPOSITES SURFACE WAVINESS. TWO ALGORITHMS BASED UPON COMPUTER VISION METHODS ARE PRESENTED FOR MEASURING THE LONG-TERM AND THE SHORT-TERM WAVINESS. THE WAVINESS INDICES COMPUTED BY THE PROPOSED ALGORITHMS RANK THE OVERALL QUALITY OF COMPOSITES EXTERIOR SURFACES. AN AUTOMATIC WAVINESS RANKING SYSTEM USED ROBUST STATISTICAL PATTERN CLASSIFICATION PROCEDURES TO CLASSIFY SURFACE SAMPLES INTO SEPARATE QUALITY CATEGORIES. EXPERIMENTAL RESULTS SHOW THAT THE PERFORMANCE OF THE COMPUTER RANKING OF WAVINESS IS COMPARABLE TO THAT OF HUMAN INSPECTORS.