A person’s stool can tell a lot about his or her state of health. Among other things, diarrhea or constipation lead to reduced digestive efficiency. Due to social developments, for many people their own stool is a shameful topic. However, the effectiveness of digestion of food has a direct influence on the recommendations for patients undergoing nutritional therapy. This paper outlines a prototypical system for an automatic and ambient classification of stool forms into three classes: thin, normal and hard stool based on the Bristol Stool Scale. The stool is recorded in transit after exiting the anus until it reaches the toilet floor to avoid the problems of conventional procedures. Corresponding data were generated under laboratory conditions. Various procedures from the field of machine learning and deep learning were applied to this data. The evaluation results show that two out of five algorithms achieve classification rates of 100%.