Object recognition is a popular task in computer vision. The method usually requires the presence of a data-set annotated with location information of the objects, which is in the form of bounding boxes around the objects. In this project, we have implemented a method to carry out object recognition in a weakly supervised manner i.e., using partially annotated data-set. The data-set provides the information about what objects are present in the image but not where they are present. We have used a Convolutional Neural Network(CNN) based architecture to perform this task. We also validated by experimenting with different architectures that mere information of presence/ absence of objects in an image (weak labels) does provide their location information for free.