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This paper considers pattern recognition methods using distributed coding. These methods permit rapid learning from a large number of training samples; their recognition speed is high regardless of the size of the learning samples. This paper presents both basic algorithm and extended algorithms. Experiments with a large database of off-line handwritten numeric patterns are then described using the power space similarity method, being a type of distributed coding. Finally the effectiveness of the technique is considered.