Nowadays, the overall well-being is considered to be one of the most important issue. The company has been taking more and more consideration in improving their employees' well-being. The employees also have been taking several approaches to improve their current well-being status. However, the well-being is usually related to the daily activity and behavior, especially in the workplace where it affects stress and mood level. In other words, the quality of a person's well-being is affected by the behavior in a workplace. In this study, we proposed a well-being recognition system where we adopted a deep learning technique to provide a non-invasive monitoring system. We classified the well-being level using three features from two surveys, which covered both stress and mood. For this preliminary study, we trained the model for both generic classification and personalized classification. The personalized approach was taken as a step to provide a personalized health decision support system, which would help raise awareness in users and encourage them to improve their behavior and eventually contribute to a better well-being. We achieved the accuracy of 83% on generic model and 91% on a personalized model.