Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y₁), osmotic solution temperature (y₂), and immersion time (y₃) were considered as the input parameters and solid gain value (x₁) and water loss value (x₂) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.