Blood serves as an indicator of health, a complete blood count (CBC) provides the clinician a view of the blood components. Diagnosis of the shape of RBC contributes information about relevant pathological diseases and condition. Red blood cells vary from the size of the cell, variations of shape, and presence of central pallor. With these observations several diseases and conditions showed correlations with the characteristic morphologic variations of red blood cells. The conventional use of peripheral blood smears remains laborious, time-consuming procedure and the lack of expertise of the microscopist is a factor to inaccurate results. The advancement of technology provided the medical field the benefits of an automated recognition and numerous studies are applying different methods in classifying RBCs. Every study differs, in what algorithm to use and what RBC is to be identified. Most of all studies were able to provide a satisfiable output and so continuous studies and development are being applied. This paper aims to develop a system that will correlate associated anemia conditions once the red blood cell was identified having an abnormality with its variations in shape or size. The proposed system was able to develop a reliable system to identify 7 different type of red blood cells normal, echinocytes, elliptocytes, dacrocytes, spherocytes, target cells, stomatocytes, and an unknown each cell achieved an accuracy of 98.33%,100%, 98.33%, 97.5%, 100%, 100%, 99.17%, and 95% respectively.