Content-based image retrieval system nowadays use color histogram as a common color descriptor. We consider color as one of the important features during image representation process. Different transformations such as changing scale of image, rotating an image and translations of image to other forms does not make any alterations to the color content of image. If we need to focus on differentiation or similarity between two images we usually deal with various color features of image. To extract color features of image we consider on color space, color reduction, color feature extraction process. In image retrieval applications, user specifies desired image as query image and wants to search for the most similar image in database of his interest. Application then identifies similar relevant images from database based on different color features of database images and query image. To achieve this we compute color features of database images and those for query image. We use local color features of different regions and combine them to represent color histogram as a color feature. These color features are compared using Euclidean distance as a metric to define similarity between the query image and the database images. For calculations of local color histogram we divide image into different blocks of size 8 × 8 as fixed, so that for each block of image spatial color feature histogram of image is obtained. Our experimental work shows that local hybrid color histogram produced more accurate image retrieval results than global color moments color histogram.