One of the toughest challenges in medical diagnosis is uncertainty handling. The recognition of intestinal bacteria such as Salmonella and Shigella which cause typhoid fever and dysentery, respectively, is one such challenging problem for microbiologists. In this paper, we take an intelligent approach towards the bacteria classification problem by using five similarity measures of fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) to examine their capabilities in encountering uncertainty in the medical pattern recognition. Finally, the recognition rates of the measures are calculated among which IFS Mitchel and Hausdorf similarity measures score the best results with 95.27% and 94.48% recognition rates, respectively. On the other hand, FS Euclidean distance yieldes only 85% recognition rate.