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Watercore is an internal disorder that leads to breakdown of tissue and possibly loss or downgrade of the product. It is very difficult to determine whether an apple contains watercore or not, especially in the early stages, based solely on external information, since watercore does not alter external texture until after severe internal breakdown. In this study, we explored the possibility of using two-dimensional (2-D) X-ray imaging to detect internal watercore damage in apples. The algorithm to detect Red Delicious watercore apples consists of two stages, the first stage extracts features from the apple x-ray image and the second stage categorizes apples into different watercore levels using the features identified. A total of eight features were extracted from an x-ray scanned apple image and these features were fed into neural network classifier to categorize them into three different classes, clean, mild, and severe. The results showed that the system was able to correctly recognize apples into clean and severe categories within 5-8% false positive and negative ratios. The result also showed that the algorithm was able to recognize apples independent of apple orientation, but only if the stem-calyx axis made a fixed angle with the x-ray beam. Sorting at random apple orientation was not tested. The estimated speed of the system, if implemented on a DSP board, will be fast enough to keep up with the current apple processing line.