Poorly constructed roads, as well as roads that are damaged with cracks or potholes, are some of the leading causes of road accidents, particularly in developing nations like India. Accurate assessment of road conditions and prompt transmission of such situations may allow interested agencies to take corrective action, reducing the number of road accidents and fatalities. With the rapid advancements in sensor technologies, data aggregation, and fusion algorithms, it is now possible to identify such road conditions and report them for prompt action by the concerned authorities. Using mobile sensing, this study aims to collect and monitor the road surface conditions in the city of Dehradun, Uttarakhand, India. In order to identify different road surfaces such as smooth roads, speed hump, and rumble strips, the collected data has been analyzed using the Artificial Neural Network technique. The proposed model has an accuracy of 97%, demonstrating the algorithm's effectiveness in detecting road surface conditions. © 2022 IEEE.