Discovering frequent patterns in multiple time series is important in practices. Methods appeared in literatures assume that the multiple time series are synchronous, but in the real world, that is not always satisfied, in most cases they are non-synchronous. In this paper, an algorithm for discovering frequent patterns in non-synchronous multiple time series is proposed. In this algorithm, first, the time series is segmented and symbolized with the linear segment representation and the vector shape clustering method,so that each symbol can represent aprimitive and independent pattenr.Thenthe minimal occurrence representaion of time series and the association rule discovery algorithm proposed by Agrawal is combined to extrac frequent patterns of various structures from non-synchonous multiple time series.Compared with the previous methods,the algorithm is more simple and flexible,and does not require time series to be synchronous.Experimental results show the efficency of the algorithm.