The anomaly detector (AD) is an important topic in the exploitation of hyperspectral image (HSI). Because unsupervised AD can detect the objects whose spectra are different from their surroundings under the conditions of no spectral libraries and atmospheric correction, it becomes increasingly important in hyperspectral military and civilian applications. Although there have been many ADs proposed, the Reed-Xialoi detector (RXD) is the most typical one, which has been successfully applied to many practical applications in terms of HSI anomaly detection. However, RXD suffers from “small sample size” problem. This paper focuses on the investigation of the three issues related to anomaly detection in real HSIs. That is, how many pixels of a target will be detected as an anomaly? Then, is an anomaly responded to its proximity? Finally, what kinds of anomalies will be detected? Some well-known RX-based detectors are programmed, such as global RX, local RX, subspace RX, weight Rx and uniform target detector. And those detectors are applied to three hyperspectral datasets acquired by both the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensors. Experimental results show that an anomaly pixel is detected, if at least one similar pixel is found at 8-surrounding pixels. The detected anomaly pixels respond to the selected background rather than to spatial neighborhood relationship. Moreover, the RX-based detectors can’t recognize detected anomaly attributes, and no one RX-based detector performance is statistically superior to the others.