Appropriate lighting is one of the indispensable elements in inspection using machine vision system. Illumination variation affects the accuracy and robustness of an inspection method that employs a machine vision system. The lighting inhomogeneity is the disturbing signal that needed to be suppressed to achieve accuracy and consistency in surface roughness quantification. In this work, the illumination compensation techniques are used for ground surface roughness evaluation by statistical texture parameters using machine vision method. The three-dimensional (3-D) surface roughness parameters are compared with the texture parameters. The experimental results are based on the ground surface images that are machined at different machining parameters. After the grinding process, the images are captured under halogen lighting. The acquired images of ground specimens are used for illumination compensation using: homomorphic filtering, Discrete Cosine Transform (DCT) based filtering and Fourier Transform (FT) based filtering techniques. This helps to suppress the low frequency components and amplify the high frequency components in order to extract the texture information. Owing the fact that the ground surfaces were weaker anisotropic surfaces, the second order statistical evaluation methods are used to extract the changes in the image texture due to the variation in surface roughness of the component. The texture parameters evaluated using these methods are correlated with the 3-D surface roughness parameters measured using an optical profiler. The texture parameters showed better correlation with the measured roughness values and this can be an integral part of any grinding system to inspect the machined components. In order to establish the homogeneity achieved after compensation of images, the inhomogeneity indicator and harmonic distortion values are calculated for the ground images. © 2019 The Authors. Published by Elsevier B.V.