We propose a novel classification framework to classify immunofluorescence images of HEp-2 cell specimens. We emphasize on using biologically motivated visual characteristics of classes, which we term as class-specific features. Given that the task involves less number of classes, a hierarchical verification based framework is employed, and is demonstrated to perform well. The current study focuses towards the classification of Homogeneous (H), Speckled (S) and Centromere (C) classes. The framework yields high classification rate with simple and efficient feature definitions. We also show encouraging performance for intermediate quality images, which represent early stage of diseases.