We model functions that use genetic information as input and trait information as output to understand genetic linkage in complex diseases. Using simulated data from GAW11, we have applied categorical classification methods and neural network analysis. We use sharing at selected markers as input, and the classification of the sib pair (for example, affected-affected or affected-unaffected) as output. In addition, our methods include environmental risk factors as predictors of phenotype. Categorical and neural network methods each led to results consistent with findings from other methods such as the logistic regression method of Rice et al. [this issue]. Post-analysis comparison with the GAW11 answers showed that these methods are capable of detecting correct signals in a single replicate. One advantage of our methods is that they allow analysis of the entire genome at once, so that interactions among multiple trait-influencing loci may be detected. Furthermore, these methods can use a variety of sib pairs rather than affected pairs only.