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In this paper, we propose a method to recognize clothing shape based on strategic observation during handling. When a robot handles largely deformed objects like clothes, it is important for the robot to recognize a constantly varying shape. Large variation in shape and complex self-occlusion, however, make recognition very difficult. To address these difficulties, we have proposed a model-driven strategy using actions for informative observation and have developed some core methods based on this strategy . In this paper, we show how these core methods can be used for an actual task that involves handling an item of clothing. In addition to proposing a sequence for this task, basic functions for realizing the sequence are also described. Using a robot, the experimental results demonstrated practical utility of the proposed strategy.