Rolling element bearings play a vital role for maintaining the reliability metrics in all rotating machineries. The downtime due to these bearing failures are now in increasing trend. In general manufacturing environment most of the time the bearings are replaced only after an indication or symptom due to the complexities of deployments for condition monitoring techniques. This paper emphasis on estimating the remaining useful life of bearing using Nested dichotomy classifier. Vibration signals were acquired for a bearing from day one of its operation till it fails naturally through a piezoelectric accelerometer and the features are extracted using the defined statistical features. The best contributing features are selected and classified using the Nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. The effectiveness of these classifiers was analyzed and compared.