In the present study a machine vision system was developed for estimating the mass of eggs arranged in a single array. A grabber frame equipped with a mirror was developed for positioning the eggs. Therefore, two images could be captured from each egg. Images were then processed by Matlab software. Six algorithms were developed to extract eggs features such as minimum, maximum and effective radii, perimeter and the frontal area from each image. The eggs were also weighed by a sensitive digital scale. Seventy percent of data after discarding the outliers were used to establish some models, and the remaining was used to verify the final model. The results showed that egg mass estimation can be accurate by using two perpendicular views of each egg. Amongst the models, one with predictors of area and effective radius was found to be the best. A high correlation coefficient was observed between eggs mass measured and predicted by the model, with an accuracy of about 95%.