The development offMRI (functional Magnetic Resonance Imaging) has led many researchers to localize brain functions using different stimuli. The use of pattern recognition techniques have made it possible to predict the stimuli being presented from the corresponding brain images and activation patterns. The primary objective of the present study was to use pattern recognition methods to develop a model using available fMRJ images and then to use the model to identify the stimulus presented from a large number of unknown images. Two different experimental conditions were used involving both binary and multi-class classification. Bilateral finger tapping data which had two distinct states "Active" and "Rest" were used for binary classification. Binary classification was done using Learning Vector Quantization (LVQ) and Least Square Support Vector Machine (LS-SVM). Gas mixture data, which were obtained from rats while ventilated with different gas mixtures for rest and breath hold task, gave various physiological conditions. These multi-class data were also classified using LS-SVM technique. Feature selection was performed on every data to select out patterns made up of significant voxels using statistical techniques like correlation, paired t-test and ANOVA. The accuracies for binary classification were between 90% and 100% while the average accuracy for multi-categorical data was 70%.