Characterizing the microbial community of water is important in different domains, ranging from food and beverage production to wastewater treatment. Conventional methods, such as heterotrophic plate count, selective plating and molecular techniques, are time consuming and labor intensive. A flow cytometry based approach was developed for a fast and objective comparison of microbial communities based on the distribution of cellular features from single cells within these communities. The method consists of two main parts, firstly the generation of fingerprint data by flow cytometry and secondly a novel statistical pipeline for the analysis of flow cytometric data. The combined method was shown to be useful for the discrimination and classification of different brands of drinking water. It was also successfully applied to detect changes in microbial community composition of drinking water caused by changing environmental factors. Generally, the method can be used as a fast fingerprinting method of microbial communities in aquatic samples and as a tool to detect shifts within these communities.