Object recognition is one of the emerging technologies in computer science. This technology uses different machine learning algorithms to classify images into their distinct categories. Garbage classification into different waste classes using machine learning and computer vision also falls under the domain of object recognition and classification. This project is a computer vision approach to classify garbage into recycling categories that could ultimately automate the sorting process and speed up the recycling pace. The accumulation of solid waste in the urban area is becoming a great concern, and it would result in environmental pollution and may be hazardous to human health if it is not properly managed. It is important to have an advanced waste management system to manage a variety of waste material. One of the most important steps in waste management is the separation of the waste into different components and this process is normally done manually by hand. To simplify the process, this project takes images of a single piece of garbage and classifies it into six classes consisting of glass, paper, metal, plastic, cardboard and trash. These six classes account for over 99 percent of all recyclable material. Using advanced machine learning algorithms (Support Vector Machine and Convolutional Neural Network) and freely available dataset, this project aims to develop an intelligent model that could automatically classify the waste material with a best accuracy of 83% (tested on the dataset). The project aims to make the separation process of the waste faster, intelligent and more efficient with minimal human involvement.