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This work discusses an approach for capturing and translating isolated gestures of American Sign Language into spoken and written words. The instrumented part of the system combines an AcceleGlove and a two-link arm skeleton. Gestures of the American Sign Language are broken down into unique sequences of phonemes called poses and movements, recognized by software modules trained and tested independently on volunteers with different hand sizes and signing ability. Recognition rates of independent modules reached up to 100% for 42 postures, orientations, 11 locations and 7 movements using linear classification. The overall sign recognizer was tested using a subset of the American Sign Language dictionary comprised by 30 one-handed signs, achieving 98% accuracy. The system proved to be scalable: when the lexicon was extended to 176 signs and tested without retraining, the accuracy was 95%. This represents an improvement over classification based on hidden Markov models (HMMs) and neural networks (NNs).