In India, 10.5% of total accident death and injury of 2016 are related to pedestrians. Identification of the vehicle, roadways, environment or human factors involved in vehicle-pedestrian crashes has become an essential factor in implementing countermeasures. Multiple Correspondence Analysis (MCA), a categorical data analysis technique was used in this study on 2016 vehicle-pedestrian accidents from the Road Accident Data Management System (RADMS) database of Chennai city to detect patterns and associations that lead to accidents. This study identifies, two key clusters and six distant clusters of variables to have factors contributing to vehicle-pedestrian crashes. The associated variables and its categories found in the key clouds were collision type, cause of accidents, junction control, and pedestrian age. The association suggests that pedestrians in the age group of 25 to 34 are mostly injured at traffic signals where the cause of the accident is usually due to non-respect of the right way of rules. Also, driving against the flow of traffic, changing lane without due care and dangerous overtaking were associated with hitting an object. Other non-trivial variables identified were the time of day, season, availability of central divider, injury severity and speed limit. This technique provides data on the associated pattern and the significance of variables that most likely resulted in a pedestrian-vehicle crash. Based on the findings, appropriate countermeasures are also suggested that could potentially help transportation safety researches and policymakers towards developing strategies that prevent pedestrian accidents. © 2020 The Authors. Published by Elsevier B.V.