Recent changes in lifestyle  have  a significant impact on dietary preferences. Nuts (walnuts, hazelnuts or pistachio) becomes essential to meet daily nutritional requirements.  Furthermore, walnuts are widely preferred in industry; however, critical food safety issues arise due to aflatoxin contamination caused by the mixing of rancid and healthy walnut kernels during the shelling process.  Therefore, it is  crucial to classify walnuts based on their kernel state (rancid and healthy) prior to shelling, in order to improve shelf life and time efficiency. In this study, it was aimed to classify unshelled walnuts using microwave propagation parameters: transmission coefficients (TCs) and reflection coefficients (RCs).  For the classification, the samples were divided into three groups: glued-unshelled walnut (GSW), healthy-unshelled walnut (HSW) and rancid walnut (RW). RCs and TCs from these groups were measured between 7-12 GHz employing Vivaldi antenna.  Obtained TCs and RCs were used to predict the walnut state through neural network.  The  accuracy was examined for TC and RC, separately, based on three different conditions: (1) training dataset ratio, (2) frequency range and (3) sample location. The minimum error rates for RW and HSW using RCs were  7.33%, 16.92%  and 7.92%,  for training ratio: 0.8, the frequency range: 11.5-12 GHz, sample placement: center point, respectively.