The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite.