Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.