The Global Positioning System (GPS) has eventually become a common positioning technology. While GPS enabled many applications, satellite signals have yet to overcome many obstacles to enable indoor positioning. Meanwhile, due to the wide deployment of Wireless Local Area Networks (WLAN) in recent years, WLAN positioning algorithms have become popular for mobile device positioning in indoor environments. The most accurate WLAN positioning algorithms exploit the so-called fingerprinting concept which consists of two stages. In the offline stage (training), the Received Signal Strength Indicator (RSSI) from a set of available Access Points (AP) is measured for a number of reference locations and stored in a database. Due to the availability of many APs and the complex structure of indoor environments, this information is distinctive for each reference location and thus is called a position fingerprint. In the online stage (testing), a mobile device receives RSSIs from the APs and their fingerprint is compared to the fingerprints that are stored in the database for a best possible match. WLAN fingerprinting can provide high accuracy in indoor environments when there are many APs which ensure fingerprint uniqueness. This paper provides a novel approach to use WLAN multipath signals as possible fingerprints for positioning algorithms for applications with lower number of access points. The paper provides an initial study.