Increasing popularity of social media platforms has led to a increase in the number of unclassified images. Given the complexity of images uploaded to these platforms and the number of classes available, it is clear that traditional image classification methods are not suitable for this kind of classification. Previous research on this topic primarily focuses on Deep Neural Networks to overcome the limitations of traditional methods. In these studies, researchers either limited the scope of their dataset; for example, handwritten digits, or combine their approach with Natural Language Processing methods to create meaningful descriptions. Similarly in this study, we use Deep Convolutional Neural Networks to classify social media images. Unlike previous approaches, there is no limitation on the scope of the images and classes represent textual tags that explain images in a simple and natural way. Moreover, the previous approaches does not allow class expansion after the training. To overcome this difficulty, a modular system is developed for classification. Separate networks are trained for each individual class and they are combined to create the overall system. Using this system new classes can be introduced without affecting the performance of the previously trained classes. Experiments are done on a dataset complied from social media platforms and this approach achieves promising results.