Independent Component Analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlies set of random variable measurements of signals. A common problem faced in the disciplines such as statistics, data analysis, signal processing and neural network is finding a suitable representation of multivariate data. The objective of ICA is to represent a set of multidimensional measurement vectors in a basis where the components are statistically independent. In the present paper we deal with a set of images that are mixed randomly. We apply the principle of uncorrelatedness and minimum entropy to find ICA. The original images are then retrieved and compared with the original images with the help of estimated error.