Image Classification is widely used in various fields such as Plant leaf disease classification, facial expression classification. To make bulky images handy, image classification is done using the concept of a deep neural network. The proposed work implemented the VGG16 model to classify an image into one of the categories like living and non-living that is further classified into several classes like an animal, human, selfie, group photos, place, wallpaper, vehicles, etc. The paper contributes a methodology for a more accurate classification of images instead of image feature extraction or image segmentation. The proposed work established a promising accuracy of 99.89%.