Outliers are records that deviate from normal behavioral pattern. This causes a serious issue when it comes to analysing data. In the recent years there has been great research to identify these outliers. Identifying them not only helps improve analysis of data but also provides many applications. The paper presents a way of indenting these outliers based on priority assigned to the attributes. The priorities are then added for each record in the dataset and the pattern is analysed. A concept based on interquartile range is used to eliminate the outliers. Hence the classifier divides the dataset into two classes: outliers and normal data.