This paper describes a neural network (NN) regression model for relative dose computation. The input signal of the NN model includes depths and field sizes and the output is the relative dose, for example, percentage depth dose (%DD) or tissue-air ratio (TAR) in this paper. After a functional link has been created, the expressing ability of input patterns and the resolution of neural networks are enhanced. The trained neural network exhibits a good generalization and interpolation ability, and it can be easily extended to other relative dose regressions. We present two examples to verify the fitness of this model. The first calculates the percentage depth dose of a 14 MV x-ray beam for field sizes of 5 cm x 5 cm to 28 cm x 28 cm. The average error computed from the NN is less than 0.47% comparing with the original measured %DD. The second example calculates the TAR of a 4 MV x-ray beam for field sizes of 5 cm x 5 cm to 20 cm x 20 cm. In this example, the training data show that the average error computed from the NN is less than 0.48%, whereas that from the testing data is less than 0.37% after training. Such an NN model can be generalized to fit data for treatment planning with any linear accelerator and can also fit data stored as tissue-maximum ratio (TMR) and tissue-phantom ratio (TPR).