Abstract: As the same as character recognition, computer automatic writer identification is a research subject involving different languages. This paper proposes a text-independent method of writer identification based on grid-window microstructure feature for different multilingual handwritings. The proposed method depicts the writing trend of local fine structures in handwritings and uses weighted distance metrics to measure the similarity between handwritings. Based on the proposed method, a text-independent handwriting retrieval system is implemented. The system is tested on the real handwriting databases of Chinese handwriting, English handwriting, Tibetan handwriting, and Uighur handwriting. The experimental results demonstrate that the proposed method is a general-purpose and weak-confined method of writer identification with high efficiency.