Writer identification recently has been studied and it has a wide variety of applications. Most studies are based on English documents with the assumption that the written text is fixed (text-dependent methods) and no research has been reported on Farsi or Arabic documents. In this paper, we have proposed a method for off-line writer identification based on Farsi handwriting, which is text-independent. Based on idea that has been presented in previous studies, in this paper we assume handwriting as texture image and a set of features which are based on multi-channel Gabor filtering and co-occurrence matrix features, are extracted from preprocessed image of documents. Experimental results demonstrate that the best result, 92% correct identification in a hit list with size 3 and 88% in a hit list with size 1, is acquired by using Gaborenergy features on Farsi handwritten documents from 25 peoples.