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Hidden Markov Models (HMM) have proven to be useful in a variety of real world applications where considerations for uncertainty are crucial. Such an advantage can be more leveraged if HMM can be scaled up to deal with complex problems. In this paper, we introduce, analyze and demonstrate Self-Similar Layered HMM (SSLHMM), for a certain group of complex problems which show self-similar property, and exploit this property to reduce the complexity of model construction. We show how the embedded knowledge of self-similar structure can be used to reduce the complexity of learning and increase the accuracy of the learned model. Moreover, we introduce three different types of self-similarity in SSLHMM, and investigate their performance in the context of synthetic data and real-world network databases. We show that SSLHMM has several advantages comparing to conventional HMM techniques and it is more efficient and accurate than one-step, flat method for model construction.