Hydraulic fracturing is a method in which fluid is pumped at elevated pressures to break down the formation, and create a conductive pathway for production of hydrocarbon fluids. Understanding the stress environment of the rock is critical for a better design and successful execution of a fracturing treatment. More often than not, a formation breakdown pressure is equal to or in close approximation of minimum horizontal or in-situ stress, also termed as closure pressure. Various analytical methods such as G-Function plot, G-dP/dG plot, square-root of time plot etc. are used for the determination of closure pressure, and have been implemented since the inception of hydraulic fracturing as a way to better design fracture treatments. These methods are prone to have subjectivity due to the experience and knowledge of the person analyzing the data, which calls for a need to more objectively analyze such data, in order to better predict the closure pressure. Machine learning is a method to teach computers to implement a predesigned algorithm and execute tasks without having to explicitly program them. It helps create significantly complex mathematical models which automate processes based on critical learning parameters, and predict within a certain acceptable degree of accuracy. In this paper, Artificial Neural Networks (ANN), a machine learning methodology, has been applied in order to minimize the subjectivity in predicting the value of closure pressure. Artificial neural networks, similar to neural networks of the brain, are a system comprising of various neurons. These neurons are organized in layers namely input layer (consisting of input neurons), output layer (consisting of output neurons or results) and multiple hidden layers. The number of input, hidden and output neurons depend on the parameters affecting the end-result. An ANN has been designed, taking into consideration critical parameters on which closure pressure depends. The model identifies and learns from the patterns in the data and predicts the required output. This output is then compared with the actual results in order minimize the error. The objective is to minimize the error so as to get a close match for the given data. In this paper we have kept the ratio of learning to testing at 80:20, which means that of all the available data, 80% is used for training the model and the rest 20% is used for testing the model. Results from this work point to the fact that the ANN was able to predict the closure pressure with reasonable accuracy. © Copyright 2018, Society of Petroleum Engineers.