Analysis on the high dimensional data is the main problem in several applications like content based retrieval, speech signals, fMRI scans, electrocardiogram signal analysis, multimedia retrieval, market based applications etc., to improve the performance of the system, the dimensions should be reduced into lower dimension. There are many techniques for both linear and non linear dimensionality reduction. Some of the techniques are suitable linear sample data and not suitable for non linear data and sample size is another criteria in dimensionality reduction. Each technique has its own features and limitations. This paper presents the various techniques used to reduce the dimensions of the data.