Quality inspection of apple fruits, traditionally performed by human experts, has to be automated by machine vision to reduce error, variation, fatigue and cost due to humans as well as to increase speed… A typical apple inspection system should employ image processing and pattern recognition techniques to precisely segment defected skin by minimal confusion with stem/calyx areas and classify fruit into correct quality category. In this thesis, we present a work performed for quality inspection of bi-colored apples using multispectral images by tackling each of these sub-problems (namely, stem/calyx recognition, defect detection and fruit grading) individually. Stem and calyx are natural parts of apples that are confused with some defects in machine vision systems. A precise inspection system requires their discrimination, which is achieved by a highly accurate support vector machines-based approach. Defect detection of apples by machine vision is very problematic due to numerous defect types present as well as high natural variability of skin color. This task is accomplished by multi-layer perceptrons (an artificial neural network), which outperformed several other methods in accuracy and speed. Final grading of fruit is obtained by binary and multi-category classification with different classifiers, where results achieved are very encouraging.