Food is an essential part of life from the beginning of civilization. People need food every day. Food is an indispensable need to being healthy and living a long life. But, consuming more food than the body needs is unhealthy habit. So, evaluation the amount of food is becoming an interest topic nowadays. Thus, a food detection and classification algorithm is proposed. In this research work, a food detection and classification algorithm is based on image processing technique. With this technique, the types of food can be classified automatically by a computer system. The purpose of the research work is to classify the food images. Food detection and classification system is useful for calories measurements and other applications. The proposed system uses the food images dataset to detect and classify the food types by using image processing technique. The image processing techniques consist of image preprocessing, image segmentation and image classification. The k-means clustering method is used to get the region of interest image (ROI). After that, the features from the ROI image are extracted by using Gray-Level Co-occurrence Matrix (GLCM), Local Ternary Patterns (LTP) and both. Then, the food types are classified by using Support Vector Machine (SVM). Finally, the percentage of accuracy and the processing time are calculated. This result shows that the accuracy is 93% and useful to examine the food types.