BACKGROUND & OBJECTIVES: Dengue epidemics have been linked to various climatic and environmental factors. Dengue cases are often found in clusters; identification of these clusters in early phase of epidemic can help in efficient control by implementing suitable public health interventions. In year 2014, Bhopal City in Madhya Pradesh, India witnessed an outbreak of dengue with 729 recorded cases. This study reports spatial and meteorological determinants and, demographic and clinical characteristics of the dengue outbreak in Bhopal City. METHODS: A cross-sectional survey of all confirmed cases reported to District Unit of Integrated Disease Surveillance Programme (IDSP), Bhopal was carried out during June to December 2014. Data pertaining to clinical manifestations, health seeking and expenditure were collected by visiting patient's residence. Geographic locations were recorded through GPS enabled mobile phones. Meteorological data was obtained from Indian Meteorological Department website. Multiple linear regression analysis was used to test influence of meteorological variables on number of cases. Clustering was investigated using average nearest neighbour tool and hot-spot analysis or Getis- Ord Gi* statistic was calculated using ArcMap 10. RESULTS: The incidence of confirmed dengue as per IDSP reporting was 38/100,000 population (95% CI, 35.2- 40.7), with at least one case reported from 73 (86%) of the total 85 wards. Diurnal temperature variation, relative humidity and rainfall were found to be statistically significant predictors of number of dengue cases on multiple linear regressions. Statistically significant hot-spots and cold-spots among wards were identified according to dengue case density. INTERPRETATION & CONCLUSION: Seasonal meteorological changes and sustained vector breeding contributed to the dengue epidemic in the post-monsoon period. Cases were found in geographic clusters, and therefore, findings of this study reiterate the importance of spatial analysis for understanding the pace of outbreak and identification of hot-spots.