Belt filter presses represent an economical means to dewater the residual sludge generated in wastewater treatment plants. In order to assure maximal water removal, the raw sludge is mixed with a chemical conditioner prior to being fed into the belt filter press. When the conditioner is properly dosed, the sludge acquires a coarse texture, with space between flocs. This information was exploited for the development of a software sensor, where digital images are the input signal, and the output is a numeric value proportional to the dewatered sludge dry content. Three families of features were used to characterize the textures. Gabor filtering, wavelet decomposition and co-occurrence matrix computation were the techniques used. A database of images, ordered by their corresponding dry contents, was used to calibrate the model that calculates the sensor output. The images were separated in groups that correspond to single experimental sessions. With the calibrated model, all images were correctly ranked within an experiment session. The results were very similar regardless of the family of features used. The output can be fed to a control system, or, in the case of fixed experiment conditions, it can be used to directly estimate the dewatered sludge dry content.