Improved wireless ZigBee network security provides a means to mitigate malicious network activity due to unauthorized devices. Security enhancement using RF-based features can augment conventional bit-level security approaches that are solely based on the MAC addresses of ZigBee devices. This paper presents a device identity verification process using RF fingerprints from like-model CC2420 2.4 GHz ZigBee device transmissions in operational indoor scenarios involving line-of-sight and through-wall propagation channels, as well as an anechoic chamber representing near-ideal conditions. A trained multiple discriminant analysis model was generated using normalized multivariate Gaussian test statistics from authorized network devices. Authorized device classification and ID verification were assessed using pre-classification Kolmogorov-Smirnov (KS) feature ranking and post-classification generalized relevance learning vector quantization improved (GRLVQI) relevance ranking. A true verification rate greater than 90% and a false verification rate less than 10% were obtained when assessing authorized device IDs. When additional rogue devices were introduced that attempted to gain unauthorized network access by spoofing the bit-level credentials of authorized devices, the KS-test feature set achieved a true verification rate greater than 90% and a rogue reject rate greater than 90% in 29 of 36 rogue scenarios while the GRLVQI feature set was successful in 28 of 36 scenarios.