現今台灣地區使用的地籍坐標系統有相當多種，在這當中最廣泛使用的為TWD67與TWD97坐標系統。由於不同時期建置的資料具有不同的地籍坐標系統，因此常需要在兩地籍坐標系統間進行坐標轉換。目前，國內正積極將地籍資料由TWD67坐標系統轉換為TWD97坐標系統。而如何在TWD67與TWD97之間進行坐標轉換，整合不同地籍坐標系統間資料之聯繫與共享，一直是國內學者致力於研究的問題。在廣泛的討論當中，最常使用的方式為利用最小二乘法求解轉換參數。 近幾年來由於神經網路技術快速的發展，提供了我們在進行地籍坐標轉換研究時新的選擇。本研究目的在於嘗試利用神經網路方式進行TWD67與TWD97地籍坐標系統；同時為了提升神經網路的效用，及解決神經網路的黑盒子問題，本研究提出利用神經網路建構網格式地籍坐標轉換模式的方法。為了驗証本研究所提出之坐標轉換方法，利用三個大小不同的實驗區之共同點資料，由不同方式轉換所得的結果顯示，以純粹利用神經網路方式所得轉換結果為佳，而網格式地籍坐標轉換模式所得結果與利用最小二乘法求解結果不相上下。 / Currently, there are two cadastral coordinate systems used in Taiwan. They are TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997) cadastral coordinate systems respectively. Frequently it is necessary to transform from one coordinate system to another. One of the most widely used method is Least-Squares with affine transformations. The artificial neural network (ANN) provides a new technology for cadastral coordinate transformation. The popularity of this methodology is rapidly growing. The greatest advantage of ANN is that it can be used very successfully with a huge quantity of data and free-model estimation that traditional transformation methods cannot be applied. In this research coordinate transformation between TWD67 and TWD97 with artificial neural network (ANN) and Least-Squares with affine transformations were examined. Besides, in order to overcome the so called ‘‘Black Box Problem’’ of ANN, algorithm of applying artificial neural network to develop regional grid-based cadastral coordinate transformation model was proposed. Three data sets with varied sizes from the Taiwan region are used to test the proposed algorithms. The test results show that the coordinate transformation accuracies using the ANN models are better than those of using other methods, such as, Least-Squares with affine transformations. The proposed algorithms and the detailed test results are presented in this paper.