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We introduce a new computational phonetic modeling framework for sign language (SL) recognition. This is based on dynamic–static statistical subunits and provides sequentiality in an unsupervised manner, without prior linguistic information. Subunit “sequentiality” refers to the decomposition of signs into two types of parts, varying and non-varying, that are sequentially stacked across time. Our approach is inspired by the Movement–Hold SL linguistic model that refers to such sequences. First, we segment signs into intra-sign primitives, and classify each segment as dynamic or static, i.e., movements and non-movements. These segments are then clustered appropriately to construct a set of dynamic and static subunits. The dynamic/static discrimination allows us employing different visual features for clustering the dynamic or static segments. Sequences of the generated subunits are used as sign pronunciations in …