Artificial Immune Recognition System (AIRS) has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of atherosclerosis, which are of great importance in medicine. The proposed system consists of the following parts: first, we obtained features that are used as inputs for Fuzzy-AIRS from Carotid Artery Doppler Signals using Fast Fourier Transform (FFT), then these obtained inputs used as inputs in Fuzzy-AIRS. While AIRS algorithm obtained 75% maximum classification accuracy for 150 resources using 10-fold cross validation, Fuzzy-AIRS obtained 100% maximum classification accuracy in the same conditions. These results show that Fuzzy-AIRS proved that it could be used as an effective classifier for the medical problems. © Springer-Verlag Berlin Heidelberg 2006.