Galaxy classification system helps astronomers with the method of grouping galaxies as per their visual form. The foremost notable being the Hubble sequence, which is considered one among the foremost used schemes in galaxy morphological classification. The Edwin Powell Hubble sequence was created by Hubble in 1926. In this project, Galaxy Image Classification using a Deep Convolutional Neural Network is presented. The galaxy can be classified based on its features into three main categories, namely: Elliptical, Spiral, and Irregular. The proposed deep galaxy architecture consists of one input convolutional layer having 16 filters, followed by 3 hidden layers, 1 penultimate dense layer and an output Softmax layer. It is trained over 3232 images for 200 epochs and achieved a testing accuracy 97.38% which outperformed conventional classifiers like Support Vector Machine and previous research contributions in the same domain of Galaxy Image Classification.