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Consumer price index (CPI) is a frequently used but not easily understood concept. Technically, CPI refers to the amount of variation in the level of market price of a basket of consumer goods and services bought by a consuming household. It can also be seen as an index of the cost of all the goods and services purchased and used by a given consumer. CPI is often used as an economic indicator that reflects the rise in prices of goods and services over a period of time. It is also seen as a performance grade of the government policies. In this work an attempt has been made to analyze the effectiveness of Neural Network models in forecasting the CPI. The data used in this work is the all-India CPI data for the period 2013 to 2017. The total number of data points used is 159 out of which 144 data points have been used for network training and 15 data sets have been used for validation. The Zaitun statistical package has been used to implement the neural network models.