Self-cleaning ability on technical surfaces can increase the added value of a product. A common path to achieve this property is making the surface superhydrophobic so that water droplets can roll down, picking up dirt particles. In this contribution, the self-cleaning efficiency of Al surfaces structured with direct laser writing (DLW), direct laser interference patterning (DLIP) and a combination of both technologies was quantitatively determined. This was performed by developing a characterization method, where the treated samples are firstly covered with either MnO2 or polyamide micro-particles, then tilted by 15 degrees and 30 degrees and finally washed applying up to nine water droplets (10 mu l) over the contaminated surfaces. Then, an optical analysis by image processing of the remaining contamination particles on the textured surfaces was realized after each droplet rolled over the surface. The DLIP textures showed the best performance, allowing the removal of more than 90% of the particles after just three droplets were released. High-speed videos and scanning electron microscopy characterization allowed a deeper understanding on the cleaning behavior and on the relationship between surface microstructure and particle size and shape.