In this paper we discuss a study comparing an algorithm implemented clinically to design intensity-modulated fields with two artificial neural networks (ANNs) trained to design the same fields. The purpose of the algorithm is to produce compensation for tangential breast radiotherapy in order to improve dose homogeneity. This was achieved by creating intensity-modulated fields to supplement standard wedged fields. Portal image data were used to create thickness maps of the medial and lateral fields, which in turn were used to design the wedged and intensity-modulated fields. The ANNs were developed to design the intensity-modulated fields from the portal image data and corresponding fluence map alone. One used localized groups of portal image pixels related to the fluence map (method 2), and the other used a one-to-one mapping between spatially corresponding pixels (method 3). A dosimetric comparison of the methods was performed by calculating the overall dose distribution. The volume of tissue outside the dose range 95-105% was used to assess dose homogeneity. The average volume outside 95-105%, averaged over 80 cases, was shown to be 2.3% for the algorithm, whilst average values of 9.9% and 13.5% were obtained for methods 2 and 3, respectively. The results of this study demonstrate the ability of an ANN to learn the general shape of compensation required and explore the use of image-based ANNs in the design of intensity-modulated fields.