The goal of this project was to work on room occupancy and to provide solutions and ideas on how to detect if a room is occupied or not using generic and non expensive sensors. This is motivated by the fact that buildings account for 50% of the total energy consumption in the US and 30% of it is wasted mainly due to a bad management of light, heating, cooling and ventilation systems. Being able to know if rooms are occupied or not would then enable us to automatize these systems and make them more energy efficient. The sensors studied in the process were a CO2, relative humidity and temperature sensor, a camera, a WiFi sniffer and a PIR(Passive infrared) sensor. All of them were implemented and tested to see how they could measure occupancy. After analyzing each sensor alone we had to combine them to obtain the best possible accuracy. To do so, two methods were tried. The first using all the sensors mentioned was tested and the accuracy achieved was 93%. The second was based on the use of neural network on images provided by the camera to detect people and the accuracy was 97%.