Aesthetic assessment of images has been getting a lot of attention for the past decade in the field of computer vision. Large amounts of social media and advertising data in the form of images is continuously analyzed to assign it an aesthetic quality value to improve businesses as well as for gaining more popularity across the web. Visual perception by humans cannot be fully replicated by a machine and continuously more work is being published on aesthetic classification of images. In this paper, we have presented a convolutional neural network model which automatically extracts high level features and distinguishes a set of images into pleasing and non-pleasing categories. Our dataset has been compiled from a variety of sources on the web to make it as diverse as possible. Compared to the traditional handcrafted methods and other machine learning models, our CNN model has provided a better classification accuracy of 68% on our dataset.