In this study a non-destructive system was designed and fabricated to detect wear and measure its parameters on railway tracks. Delayed detection of wear on railway lines can cause critical problems and makes the repair and maintenance process more time and cost-consuming. Even worth, if the wear passes the critical value, it changes the geometry of the line, causing the derailment of the train. In the proposed method for wear detection, a system with a line laser and a camera is applied. The laser lights the surface of the rail, and the camera captures an image from it. By processing the shape of the lit pattern, which is different in worn and unworn areas, we could detect wear and estimate its several parameters. In the proposed method, after applying some preprocessing techniques to extract the shape of the lit pattern, an artificial neural network (ANN) is used to quantify w1, w2, and w3 as the three parameters of the wear. The performance of three artificial neural networks (MLP, GRNN, and RBF) to estimate w1, w2, and w3 was studied. Among all, GRNN had the best performance with the maximum error of 0.27, 0.24, and 0.32 mm, respectively for W1, W2, and W3. It shows the high efficiency of the suggested measurement system. In RDD-S11, which is currently being used in lines one and two of Mashhad Urban Railway Company to detect three common defects, the proposed method is being used to detect and measure the wear on railway tracks.