This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attrib-utes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibil-ity, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attrib-utes such as diameter at breast height (DBH ), height (H), crown base height (CBH ), crown length (CL ), social posi-tion (SP ), stoniness (ST ), position on the relief (PR ), vitality (VT ) and branch arrangement (B A ) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to D BH , with 0.76 as coefficient of determination ( R2 ), 0.540 as Mean Absolute Error (MAE ) and 22.4 root-mean-square error in percentage (RMSE％ ); (2) the trend changed according to bark colour, with significant differences for the intersection (β0 – Pr > F: p = 0.0124) and slope (β1 – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: DBH (ρ = 0.88), H ( ρ = 0.58), C BH (ρ = 0.46), SP (ρ = ? 0.52), and BA (ρ = ? 0.32); (4) modelling with ANN confirmed high adjustment ( R2 = 0.99) and accuracy (RMSE％ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest men-suration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species.