Hyperspectral images are collected by hundreds of contiguous spectral channels and thus, the data volume to be processed is considered to be huge. With such high spectral resolution, spectral correlation among bands is expected to be very high. Band selection (BS) is one of common practices to reduce data volumes, while retaining desired information for data processing. This paper investigates issues of band selection and further develops various exploitation-based band prioritization criteria (BPC) which rank the hyperspectral bands in accordance with priorities measured by various applications such as detection, classification and endmember extraction. Three categories of BPC can be derived based on different design rationales, (1) second order statistics, (2) higher-order statistics, and (3) band correlation/dependence minimization or band correlation/dependence constraint. Unlike commonly used band selection techniques which do not specifically use the concept of band prioritization (BP) to select desired bands, this paper explores the idea of BP for band selection where an appropriate set of bands can be selected according to priority scores produced by spectral bands. As a result, the BPC provides a general guideline for band selection to meet various applications in hyperspectral data exploitation.