In this paper, we present a new area-based stereo matching algorithm that computes dense disparity maps for a real time vision system. While many stereo matching algorithms have been proposed in recent years, correlation-based algorithms still have an edge due to speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation-based algorithms. We use two correlation windows (one large and one small size) to improve the performance of the algorithm while maintaining its real-time suitability. Unlike other area-based stereo matching algorithms, our method works very well at disparity boundaries as well as in low textured image areas and computes a sharp disparity map. Evaluation on the benchmark Middlebury stereo dataset has been done to demonstrate the qualitative and quantitative performance of our algorithm.