Raman spectroscopy has been proved to be a promising diagnostic technique for various cancers detection. A major drawback for its clinical translation is the intrinsic weakness of Raman effects. Highly sensitive equipment and optimal measurement conditions are generally applied to overcome this drawback. However, these equipment are usually bulky, expensive and may also be easily influenced by surrounding environment. In this preliminary work, a low-resolution fiber-optic Raman sensing system is applied to evaluate the diagnostic potential of Raman spectroscopy to identify different bladder pathologies ex vivo. A total number of 262 spectra taken from 32 bladder specimens are included in this study. These spectra are categorized into 3 groups by histopathological analysis, namely normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors. Principal component analysis fed artificial neural network are used to train a classification model for the spectral data with 10-fold cross-validation and an overall prediction accuracy of 93.1% is obtained. The sensitivities and specificities for normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors are 88.5% and 95.1%, 90.3% and 98%, and 97.5% and 96.4%, respectively. These results demonstrate the potential of using a low-resolution fiber-optic Raman system for in vivo bladder cancer diagnosis.