The Electronic tongue (ET) composed of different kind of potentiometric chemical sensors has been applied for the detection of urinary system dysfunctions and creatinine levels. The creatinine contents evaluated by ET were compared with those obtained by automated Jaffe's method and GC-MS, obtaining a satisfying agreement for both methods. Partial least square regression discriminate analysis (PLS-DA) and feed forward back-propagation neural network (FFBP NN) classified 51 urine specimens from healthy volunteers in four classes, according to the creatinine content, showing that both techniques can satisfactorily differentiate urines according to this parameter. The best accuracy result of 92.2% correct classification of unknown samples was achieved with FFBP NN. Moreover, the possibility of ET system to distinguish between urine samples of healthy patients, and those with malignant and non-malignant tumor diagnosis of bladder has been shown.