Rapid development of nanosatellites has led to many unique and innovative space applications. These tiny satellites have come a long way since Sputnik, the first satellite that was launched in 1957, weighing 83 kg. The success of the CubeSat has revolutionized space technology. Typically, these CubeSat fall into categories of nanosatellites weighing no more than 1.33 Kg and using less power than a five watt light bulb. Mostly, CubeSat has one or more payloads such as Imaging Payload, Scientific payload and High Frequency (HF) Radio Beacon.With the Imaging payload their task is to capture low or high resolution images of the earth observation missions. With the steadily increasing demand for CubeSat imaging payload missions, several nano-satellites have been launched, and thousands of low/high resolution images are acquired every day and transmitted to ground stations. This leads to an exponential increase in the number of low/high resolution images in databases. Therefore, how to retrieve useful images quickly and accurately from a huge and unstructured image database becomes a challenge.In this project, it proposes the use of a content-based image retrieval (CBIR) system relying on a combination of the three low level features such as color, shape and texture features. In order to accurately classify and retrieve useful information in a huge image database. The evaluation performance of the proposed system when compared to other existing systems provides a precision value of 1 or 100%. It can be concluded that the proposed system is able to classify images and retrieve results in a reasonable time of at least 15 seconds.