In our previous work of writer identification, a database of handwriting samples (written in English) of over one thousand individuals was created, and two types of computer-generated features of sample handwriting were extracted: macro and micro features. Using these features, writer identification experiments were performed: given that a document is written by one of n writers, the task is to determine the writer. With n = 2, we correctly determined the writer with a 99% accuracy using only 10-character micro features in the writing; with n = 1000, the accuracy is dropped to 80%. To obtain higher performance, we propose a combination of macro and micro level features. First, macro level features are used in a filtering model: the computer program is presented with multiple handwriting samples from a large number (1000) of writers, and the question posed is: Which of the samples are consistent with a test sample? As a result of using the filtering model, a reduced set of documents (100) is obtained and presented to the final identification model which uses the micro level features. We improved our writer identification system from 80% to 87.5% by the proposed filtering-combination technique when n = 1000.