Optimized superhydrophobic and self-cleaning nanocomposite surfaces were obtained by spraying surface modified ZnO nanoparticles (NPs) onto PDMS, using octadecylphosphonic acid and octadecanethiol as hydro-phobic modifiers. In this study, it is the first time to our knowledge that surface parameters such as topography, morphology, superhydrophobicity, and self-cleaning are correlated to particle surface distribution and agglomeration parameters obtained by image analysis. The topography, morphology, and wettability of the surfaces were analyzed using atomic force microscopy, scanning electron microscopy, static contact angle (SCA), and contact angle hysteresis measurements. Image analysis was performed using the new enhanced graphical user interface of a previously self-developed Matlab (R) algorithm. Both hydrophobization methodologies increased the NPs' surface coverage and the hierarchical rough structure formation on the substrates, resulting in more homogenous superhydrophobic self-cleaning surfaces. A higher coated fraction and lower degree of interconnected uncoated PDMS paths are correlated to an increase in SCA. The combination of a higher ag-glomerates fraction, lower agglomerate radius, and lower distance between agglomerates obtained for the sur-faces with hydrophobized ZnO-NPs rendered self-cleaning surfaces. The observed correlations increase the understanding of the design and modelling of superhydrophobic self-cleaning PDMS/ZnO nanocomposite sur-faces for use in high voltage outdoor insulators.