In this study, each chest radiograph was processed by a three- dimensional Gaussian-like matched filter. Edge tracking and region growing techniques were applied to the filtered image to segment all possible nodules. The area and its boundary were then divided into 36 sectors (i.e., 10 degrees per sector) using 36 equi-angle dividers radiated from the center. For each suspicious area, we computed radius, average gradient within the sector, average gradient near the boundary, and contrast were computed features within each 10 degree sector. A total of 144 computed features for one suspicious area were used as input values for a newly designed three layer neural network to perform pattern recognition studies. The neural network system was constructed to emphasize the correlation information associated with the features. In this part of the research, several circular path neural network connections between the input and the first hidden layers were linked. These included (1) self correlation networking and (2) neighborhood correlation networking. The networks for self correlation and neighborhood correlation were designed to extract the common factors within the sector and between sectors, respectively. In this study, neighborhood correlation across sectors of 20 degrees, 30 degrees, 40 degrees, and 50 degrees were used. We have tested this approach on the JPST chest radiograph database consisting of 154 chest radiographs using the grouped jack-knife method. The performance in detecting medium-sized nodules was 75% in sensitivity at 5.9 false-positives per image. The performance remained the same for large nodules (with 75% sensitivity at 5.6 false-positive per image). This work presents a new and effective way in analyzing tumor objects. Instead of lumping global features for each object and analyzing them by a conventional classifier, the new method computes features in sectors and analyzes them using a fan-oriented neural network. We also found that the MCPNN technique performs slightly more effective in detecting larger nodules than smaller nodules.