Grinding characterized by low material removal rate and high surface finish is an abrasive machining process. Geometrically undefined grits of grinding wheel influences part surface finish. Cutting edges worn out during grinding are retained by timely dressing. Operators on shop floor decides on the dressing interval based on end of wheel life such as burns, chatter marks and deterioration in the workpiece surface finish. Incorrect dressing of grinding wheel increases unnecessary machining time and wheel wastage. The present work defines the usefulness of grinding force signal and ground surface texture analysis in the estimation of grinding wheel redress time. Grinding experiments were performed in surface grinding machine with white Alumina wheel on D2 tool steel specimen. During the experiments, the force signals were acquired from 9257B Kistler dynamometer, for each grinding pass with 16 KHz sampling frequency. Ground workpiece surface images were captured using multi-sensor Coordinate Measuring Machine (CMM). Experiments were continued till the grinding wheel reaches its end of life. Grinding force signal features prominent to the assessment of grinding wheel deterioration in time, frequency and time-frequency domains were extracted based on prognostic metric evaluation. Hough transform based image texture features were also extracted. The test results depict that a good correlation exists between grinding force signals and ground surface images in measuring the wear deterioration level and thus the ground surface features can be considered as an explicit criterion in the redress life estimation of the grinding wheel. © 2018 The Authors. Published by Elsevier B.V.