Time series analysis and forecasting is an efficient versatile tool in diverse applications such as in economics and finance, hydrology and environmental management fields just to mention a few. Among the most effective approaches for analyzing time series data, the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA) was employed in this study. The paper outlines the practical steps which need to be undertaken to use Autoregressive integrated moving average (ARIMA) time series models for forecasting the monthly inflation in Vietnam. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. To investigate the performance of the forecasting model, a numerical dataset of inflation was collected monthly from the General Statistics Office of Vietnam (GSO) for the period from January 2004 to July 2014 are examined. The examination result shows that average inflation rate in the second half of 2014 tends to be higher than in the first half of 2014 and the average annual inflation is 5.98% based on the mean absolute percentage error (MAPE) criteria.