Classification is a steady practice for allocating a given piece of input into any of the known class. Classification is an important machine learning technique. Many classification problems exist in different application areas and need to be solved. This paper evaluates the proficiency of different memory based classifiers for classification of multivariate data set with missing values. For the proficiency evaluation the data sets with missing values have been taken from UCI machine learning repository and evaluated using the open source machine learning tool. Different memory based classifiers has been compared and a practical guideline for selecting the renowned and more suited algorithm for a classification is presented.