This report deals with automatic image colorization. Image colorization is an ill-posed problem that usually requires user intervention to achieve high quality. Two basic fully automatic approaches are proposed that are able to produce realistic colorization of an input grayscale image. Firstly, the automatic colorization was proposed using Support Vector Regression and Markov Random Field. The second approach is based on the convolutional neural network which is motivated by the recent success of deep learning techniques in image processing. Support vector regression is used to predict the U and V color channels for given pixel value, whereas a feed forward convolutional neural network is used in the second approach that predicts the a and b color channel values for lab color space of the input pixel which will be finally converted to RGB. Going through both approaches for around 200 trained data, if input image contains multiple objects, output from CNN seems closer to original image than output from SVR. The analysis is based on histogram comparison using various methods like Correlation, Chi-square, Intersection and Bhattacharya distance (also known as Hellinger).