Have you ever looked at photos and thought, hey this is pretty, I wonder where this was taken? Or perhaps you just came back from vacation and you took many beautiful pictures, but it is a hassle to sort through all of them. You wonder if there's a way to identify your photos based on the landmarks pictured. This is what our project aims to solve. In this project, we want to use machine learning to identify and classify landmarks from images. Our model of choice is the convolutional neural network, or CNN for short. Now what is a CNN, you may ask? It is a type of neural network commonly used to analyze and process image data. Consider the story of the blind men and elephant. Each blind man is only able to feel and obtain information about a small part of the animal. However, if they discuss and learn from one another, they will be able to obtain the full image of the animal and identify it as an elephant. Likewise, for a CNN, each neuron receives chunks of the input image, which are then convolved through multiple hidden layers to learn features, and lastly it outputs a classification for the image. Our dataset originates from the Kaggle Google landmark recognition challenge, where there are 1.58 million images and over 81,000 unique landmarks. However, the number of samples across classes is imbalanced, ranging from 1 to a few thousand per class. Hence we only consider classes with more than 1000 samples, which leaves us with about 15,000 images. As a result, our trained model is left with 7 unique landmarks to classify. After experimenting with various CNN bases, we decided to apply transfer learning on our model using ResNet 50V2, at the same time training with different epochs and learning rates, and a combination of both. These models were evaluated using various accuracy metrics, precision, recall, F1 score and support. From our experiments, we managed to achieve a high weighted average accuracy of about 95%. Our model performed relatively well on a subset of the data. However, there is still room for improvement by scaling up to classify on more unique landmarks, and exploring other evaluation metrics.