A new block matching algorithm (BMA) especially appropriate for large search areas is proposed. Motion vectors of causally adjacent blocks can be credible motion vector candidates in continuous motion fields. However, they are not helpful for searching complex or random motions. In order to remedy this problem, we propose a new two-step hierarchical block matching algorithm using spatial correlation in a motion field. In the first step, the candidates for an initial estimate consists of four motion vectors of adjacent blocks for searching continuous motion, and regularly sub-sampled points for searching complex or random motions. In the second step, the estimate is refined within a smaller search area by using full search BMA (FS- BMA). The straightforward application of the first step, however, tends to break data flow regularity due to random locations of four adjacent motion vectors. Therefore, in order to maintain consistent data flow in examining the four adjacent vectors, we introduce partial mean absolute difference which is calculated by suing a partial searching block rather than the whole block. Simulation result show that, in comparison with FS-BMA, the proposed algorithm reduces the computational complexity to 5.9 percent with negligible PSNR degradations. Furthermore, due to its regular data-flow, our scheme is especially suitable for hardware implementation.