This paper presents our new result of a study on machine-aided classification of four critical Gleason patterns with curvelet-based texture descriptors extracted from prostatic histological section images. The reliable recognition of these patterns between Gleason score 6 and Gleason score 8 is of crucial importance that will affect the appropriate treatment and patient's quality of life. Higher-order statistical moments of fine scale curvelet coefficients are selected as discriminative features. A two-level classifier consisting of two Gaussian kernel support vector machines, each incorporated with a pertinent voting mechanism by multiple windowed patches in an image for final decision making, has been developed. A set of Tissue MicroArray (TMA) images of four prominent Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3 and 4 + 4 has been studied in machine learning and testing. The testing result has achieved an average accuracy of 93.75% for 4 classes, an outstanding performance when compared with other published works.