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We propose an approach to recognize time-series gesture patterns with Hierarchical Self-Organizing Map(HSOM). One of the key issue of the time-series pattern recognition is to absorb the time variant appropriately and to make clusters which include the same gesture class. In our approach, we arrange the SOM hierarchically. In each layer of the SOM the time-series patterns divided into some periods; postures, gesture elements and gestures. They are learned in each layer of HSOM. For example, postures are learned in the first layer, gesture elements are learned in the second layer and so on. Using the sparse code in the bottom layer, the SOM can perform time invariant recognition of the gesture elements and gestures.