A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional data. This paper proposes a way of directly handling high-dimensional data in the FCM clustering and classification. The proposed classifier without any preprocessing outperforms the k-nearest neighbor (k-NN) classifier with PCA on the benchmark set of COREL image collection.