Similarity join over multiple time series is an interesting task of data mining. This task aims at identifying couples of similar subsequences from multiple time series and the two subsequences might have any length and be at any position in the time series. However, the task is extremely challenging since the computational time to search for couples of similar subsequences from two time series is very large. Moreover, the task needs to normalize two subsequences before conducting a distance measure on the normalized subsequences to consider the similar degree of the original subsequences. To address the problem, this paper proposes a method of similarity join over two time series under Dynamic Time Warping (DTW), supporting z-score normalization. The proposed method utilizes both a suite of state-of-the-art techniques for computing the DTW distance and a technique of incremental z-score normalization to reduce the computational costs. The method employs multithreading to improve runtime performance. If similar subsequences from two time series may not pair up because they are too far apart, the method might use a sliding window to constrain a scope for coupling similar subsequences. The experiments on the proposed method show that the method could return similar subsequences quickly and incur no false dismissals.