Searching the optimal decoding for Interactive Sequential Pattern Recognition tasks may be computationally expensive. A normalization procedure is proposed so that computing optimal hypotheses becomes more efficient. A specially profitable case is reported when dealing with Regular Languages. Experimental results show a significant save of computational time.