Movement and social life of wheelchair users are constrained by their disability and suitability of paths they can move on. Modern electric wheelchairs offer them assisted drive, making their movement easier and longer. They, however, do not prevent accidents, injuries, and inconveniences caused by path roughness and ramp slopes. Providing information about suitability and accessibility of paths and buildings for wheelchair users will enable them to beforehand plan their trip to not to be caught by surprises or not to take a trip all together. The recent emergence of smartphones equipped with inertial sensors offers new opportunities for provision of information regarding quality and accessibility of paths and buildings for wheelchair users. To this end, we propose a smartphone-based participatory system incorporating a hybrid unsupervised machine learning technique based on Self Organized Maps (SOM) to identify path conditions and to create clusters of similar path types. Our solution provides useful information about the angle of the ramp and curb slopes as well as pavement quality and roughness and path types.