The official Indian accident statistics show that the number of road accidents and fatalities are one of the highest worldwide. These official statistics provide important facts about the current accident situation. It is suspected that for various reasons not all accidents are reported to the official statistic. This study estimates the degree of underreporting of traffic accidents with casualties in India. In order to get a national overview of the traffic accident situation it is necessary to improve the knowledge about underreported accidents. Therefore, the in-depth accident database of “Road Accident Sampling System India” (RASSI) was analyzed . This project is organized by a consortium that has collected traffic accidents scientifically in four different regions since 2011 on the spot which have been reported either by police or by local hospitals and own patrol by RASSI engineers. Thus, the level of underreporting is researched by comparing data from hospital records and police records in order to estimate the number of accidents which are not documented in official statistics. Based on a number of around 1 635 accidents mainly in rural area it was found that 32% of all documented cases are not recorded by the national police. Based on these findings it is assumed that the national statistics cover only about two-third of all rural crashes in India. Various characteristics of accidents can influence the percentage of underreported cases. Accident scenarios - e.g. classified by Accident Type - have different shares of underreported cases. RASSI data shows that every 2nd single vehicle accident (including pedestrian cases) is not notified in the official statistics.In the study the accident data is analyzed in detail concerning: Accident scenariosRoad user categoryInjury severityRoad conditionsInfrastructural detailsResult of this study is an overview of the accident situation in rural area and point out the frequency of underreported accidents depending of typical accident characteristics. This information can be used to identify main causes of this phenomen.