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Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, evolving neural network model with dynamic time warping piecewise linear representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base, then evolving neural network model will be applied to train the pattern and retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system integrating DPLR and evolving neural networks can make a significant and constant amount of profit when compared with other approaches using stock data.