The Kohonen self-organizing neural network is a useful tool for pattern recognition. Based on the Kohonen map obtained from the training set, predictions can be made for the unknown objects. The problem is how to determine the membership of new objects hitting empty neurons which were not activated by any training set objects. The K-nearest neighbor technique has been previously used to solve this problem based on the relative geometric position of the neurons in the two-dimensional Kohonen map (Kohonen-KNN). However, during the projection into a low-dimensional subspace for the Kohonen neural network, some information about the correct neighbor relationships between the object vectors is lost. Thus, the Kohonen-KNN method may not give the best prediction results. In this paper, an alternative method is proposed for the Kohonen neural network to be used in a supervised way based on the weight interpretation (Kohonen-WI). The membership of the samples hitting the empty neurons during the prediction process is determined to be the same as that of the nearest active neuron based on a distance measure from the trained weight vectors. The Italian olive oil data set is used to test this method. Moreover, the learning vector quantization (LVQ) method has also been used to treat the same data set. This method explicitly uses the class membership of samples in the training set for the fine adaptions of network weights. It has been found that the Kohonen-WI method gives better prediction results than Kohonen-KNN, indicating that the weight interpretation can partly compensate for the information loss about the correct neighbor relationship between the neurons in the Kohonen map. The LVQ method gives very similar and satisfactory classification results as Kohonen-WI, though their working mechanisms are different. By comparison, the Kohonen-WI method is easier to use.