Human Epithelial type-2 (HEp-2) cells are used as substrates for the detection of Anti Nuclear Antibodies (ANA) in the Indirect Immunofluorescence (IIF) test to diagnose autoimmune diseases. Pathologists in the laboratory examine the IIF slides to detect and recognize theHEp-2 cell patterns to generate the report. So, the IIF test is subjective and requires objective analysis. This paper introduces a novel algorithm for HEp-2 cell pattern recognition, which can be embedded into the CAD. This is the first time HEp-2 cell Classification system separated intermediate and positive cells before preprocessing and processed further for classification to achieve better accuracy. The design used spectral, statistical and textural features along with Artificial Neural Network (ANN) for classification. The algorithm introduced in this paper was tested using ICPR 2016 IIF HEp-2 cell dataset of frequently occurring six distinct fluorescence patterns by computer simulation. Two experiments were conducted; the results concluded that features performance linearly increases with combination. The experimental results proved that Hybrid feature set approach based HEp-2 cell classification is more reliable with 93.15% accuracy on the ICPR 2016 IIF HEp-2 cell images dataset.