Accurate sales forecast is crucial to plan the production process, especially in automotive industry, where a large number of factors –macro-economic, micro-economic and others dynamically influence the demand.Forecasting in such a dynamic industry, especially with the onset of COVID, has become extremely important with the preference for personal transportation on the increase. In this paper, a neural network model to forecast the domestic retail sales of passenger vehicles in India is formulated. A Time Delay Feed Forward Neural Network with Back Propagation (TDNN)is used and the forecasting accuracies determined by comparing the values of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The macro-economic indicators identified were Unemployment rate, GDP growth rate, Private Final Consumption Expenditure (PFCE), Index of Industrial Production (IIP), Bank Lending Rate (BLR), Inflation Rate and Crude oil prices. The indicators were taken as the input variables and Passenger Vehicle Retail Sales was selected as the target variable to formulate the TDNN model. It was observed that the TDNN model was able to forecast the output with great accuracy.