Summary: Kernel factor analysis (KFA) with varimax is proposed by using Mercer kernel functions which can map the data in the original space to a high-dimensional feature space, and is compared with kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with varimax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition.