Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility test usually use egg candling. The purpose of observation is to choose eggs fertile  (eggs contained embryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered into the incubator for hatching eggs and infertile can be egg consumption. However, there are obstacles in the process of sorting the eggs are less time efficient and inaccuracies of human vision to distinguish between fertile and infertile eggs. To overcome this problem, it can be used  Computer Vision technology is having such a principle of human vision. It used to identify an object based on certain characteristics, so that the object can be classified. The aim of this study to comparasion classify image fertile and infertile eggs with SVM (Support Vector Machine) algorithm and K-Nearest Neighbor Algorithm based on input from bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-ocurance Matrix).  Eggs image  studied are 6 day old eggs. It is expected that the proposed method is an appropriate method for classification image fertile and infertile eggs.