A planar-dimensions vision measurement method is proposed by developing a Neural Network to measure real-world distance between any two points on the plane. The system leveraging Neural Network ability to search in the solution space is a highly non-linear model that could map points’ location on the pixel plane of image(s) with the actual distance between them considering the non-uniform geometric distortion in captured images caused by the entocentric lens in a common camera. The method was tested with a printed calibration chessboard, placed in different locations on the plane, with measured distance between tested points. Experimental results show the proposed method’s mean absolute error is 1.24 × 10^-2 mm and standard deviation is 1.63 × 10^-3 mm, tested with 10-folds cross-validation method.