Neural network (NN) technology was applied to digital image analysis data for 112 Papanicolaou-fixed and -stained smears of lymphocyte-rich effusions (LREs). The smears were analyzed with an inexpensive image analysis system assembled in our laboratory. Several models were developed using backpropagation NN development software in an effort to optimize classification of the LREs as reactive lymphocytosis or malignant lymphoma and to analyze the effects of various parameters on classification rates. The greatest specificity and sensitivity of LRE classification were achieved with NN models that consisted of 7 input neurons, including 5 morphometric and 2 densitometric variables, 10 hidden-layer neurons and 1 output neuron. This NN architecture with a sigmoidal transfer function provided a true cross-validation rate of 89.3% of testing data, with a sensitivity of 76.9%, specificity of 93.0% and shrinkage of 10.7%. The same NN architecture with a step transfer function provided a true cross-validation rate of 95.3%, sensitivity of 85.7%, specificity of 97.6% and shrinkage of 0%. The effects of various parameters, such as network size, shrinkage and ratio of sample size to input layer size, on NN accuracy are discussed.