In order to predict the residual life of offshore platforms and establish efficient schedule for underwater inspection and repair, it is necessary to estimate the fatigue crack growth rate in tubular joints properly. Linear Elastic Fracture Mechanics and Stress Intensity Factor are applicable tools for evaluating growth rate of existing fatigue cracks in offshore tubular joints. In the past several approaches based on Paris crack growth law, have been proposed in this regard. Each of these approaches use different methods for estimating the Stress Intensity Modification Factor (Y). In this research the capability of Artificial Neural Networks for evaluating the fatigue crack growth rate in offshore tubular T-joints under axial loading is investigated, when the crack depth is more than 20% of chord wall thickness. For this condition the crack growth process is highly affected by joint geometry and loading mode. Two types of artificial neural network are developed for predicting the Y factor: Radial Basis Function (RBF) and Multi Layer Perceptron (MLP) networks. The required input data consist of the crack shape and the percentage of crack penetration through thickness. Experimental data from NDE center in University College London are used for training and testing the networks. The results of this research are compared with other existing theoretical and empirical solutions.