Abstract-A high recurrence rate, and progression to higher stages are observed for patients diagnosed with urothelial carcinoma (previously known as transitional cell carcinoma). Low prognostic value of the current grading systems result in extensive follow-up of patients for multiple years after first diagnosis. Although, the aid of computer systems for prognosis prediction of superficial urothelial carcinomas have been proposed, earlier analyses have been focused on using morphological features of cells and attributes describing the patient. In this study, we propose a system to aid in the prediction of prognostic information based on a texture analysis of histopathological images of superficial urothelial carcinoma. The analyses are conducted using the local binary pattern (LBP) and local variance (VAR) operators followed by a RUSBoost classifier. A dataset of 42 patients, consisting of 13 patients without recurrence, 14 with recurrence but not progression and 15 patients with progression are studied. Using a leave-one-out cross-validation, an accuracy of 70% and sensitivity of 84% is achieved.