Connectionist networks may provide useful models of stimulus equivalence and transfer of function phenomena. Such models have been applied to a range of behavioral tasks and have demonstrated transfers of function via equivalence relations following appropriate training, with networks accurately simulating the behavior of human subjects. In the current study, a connectionist network was pretrained on a series of equivalence and sequence tasks to simulate the preexperimental experience of an adult subject. It was then exposed to the equivalent of six conditional discriminations, and was tested for the formation of three 3-member equivalence classes (corresponding to A 1-A2-A3, B1-B2-B3, C1-C2-C3). It was subsequently trained to produce a pair of four part sequences (corresponding to B1 -+B2-+Ct1 -+B3 and B3-+ B2-+Ct2-+ B1 , where Ct1 and Ct2 represented contextual cues) before being tested for transfer, through equivalence, of the sequence responses to the C stimuli. Following appropriate pretraining, the network showed the formation of three equivalence classes and a transfer of sequence function to the nontrained C stimuli (producing the novel sequences C1 -+C2-. Ct1 -+C3 and C3-+C2-+Ct2-+C1) . A control network, which was not exposed to conditional discrimination training, failed to demonstrate equivalence and the transfer of sequence function, as predicted by findings from experimental demonstrations with human participants. Network performance was analyzed as a function of amount of pretraining and a number of psychologically plausible training methods are presented. The data suggest that connectionist networks may provide accurate and plausible models of stimulus equivalence and transfer of function phenomena in natural language.