Writer identification is a popular research field in many languages such as English, Persian, Chinese, etc. The approaches of writer identification methods are dependent on the language because different languages letters have different pattern. In this paper, we have presented XGabor filter and proposed a language independent writer identification system. In the feature extraction phase of proposed method, Gabor and XGabor filters are used while in the classification phase, a new classification method is defined that is not based on any kind of distances among feature vectors. The proposed classifier uses the sequence similarity of the Sorted Order of Features (SOF). To measure this similarity, the Longest Common Subsequence (LCS) algorithm is employed. In simulation phase, two databases in different languages have been used. First one consisted of 100 people's Persian handwritings and second one had 30 people's English handwritings. The accuracy of the system was satisfactory in both of them.