As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, due to ubiquity of mobile devices such as smart phones, the health monitoring applications are accessible by the patients practically all the time. We have created a semi-automatic food calorie and nutrition measurement system via mobile that can help patients and dietitians to measure and manage daily food intake. While segmentation and recognition are the two main steps of a food calorie measurement system, in this paper we have focused on the recognition part and mainly the training phase of the classification algorithm. This paper presents a cloud-based Support Vector Machine (SVM) method for classifying objects in cluster. We propose a method for food recognition application that is referred to as the Cloud SVM training mechanism in a cloud computing environment with Map Reduce technique for distributed machine learning. The results show that by using cloud computing system in classification phase and updating the database periodically, the accuracy of the recognition step has increased in single food portion, non-mixed and mixed plate of food compared to LIBSVM.