The global objective of this study was to investigate the best features of the surface topography for fatigue damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain slip and a crack initiation at the surface of the machined structure and finally leads to fatigue failures. Therefore, the surface topography has a major influence on the fatigue strength of the machined structure. An optical confocal measurement system (Alicona) was applied to measure the surface topography parameters. These parameters are the arithmetical mean height S a , the root-mean-square height S q , the maximum peak height S p , the maximum valley depth S v , the maximum height S z , and ten-points height S z 1 0 . In this paper, feature selection using the Pearson correlation method was adopted to select the best surface textures that provide best the neural network (NN) model performance.The proposed NN models have been trained using the scaled conjugate-gradient back-propagation method. Results showed that the best surface topography parameters were S a , S v , S 1 0 Z , S z , where the NN model can detect and classify the damage with an accuracy of up to ∼94 .4%.