Missing values can cause incorrect conclusions about data. Correspondingly, substitution of the missing values may introduce inaccuracies and irregularity. In the final result, a data value missing is the main drawback. Due to this problem, the system performance has the highest error rate in other words it degrades the system performance. Additionally, most of the analysis methods cannot be performed if there are missing values in the data. Some of the techniques are explained in this paper. The main goal of this paper is to present an overview of the missing value imputation techniques.