The present study has been performed in Italy, in stands of the main broad-leaved forest species. Thinned and unthinned stands of Quercus cerris L. (6.3% of total area covered by deciduous species in Italy), Castanea sativa L., (8.5%) and Fagus sylvatica L. (12.6%) were selected from 15 permanent plots. LAI data have been collected during the 3 years (1993–1995), using both direct (littertraps) and indirect methods (LAI-2000 Plant Canopy Analyzer, PCA, Li-Cor, Lincoln, NE, USA). LAI estimation by litter collection (LAILT, 3.2–7.6 m2 m−2) was in the range of values reported for deciduous forest, while the PCA method generally underestimated the LAI (LAIPCA, 1.8–5.8 m2 m−2). Average underestimation was 26.5%, being similar to other reports. The underestimation was higher in thinned (29.1%±14.7%) than in unthinned (22.2%±12.2%) stands and in stands characterised by a LAILT>5 m2 m−2. On the contrary, in stands with LAILT<5 m2 m−2, PCA estimates were closer to littertraps ones (−11%±9.6%). On the average, LAILT was 4.51±0.92 m2 m−2 and 5.86±0.11 m2 m−2, for thinned and unthinned stands, respectively. Also PCA was able to estimate this difference, arriving at 3.14±0.70 m2 m−2 (thinned stands) and 4.47±0.61 m2 m−2 (unthinned stands). With both methods, the difference between the two stand types was strongly significant. Although LAIPCA values were always below LAILT, the correlation between the two data sets was linear and significant. When recalculated omitting the reading of the external PCA ring, the correlation between LAILT and LAIPCA improved, and the underestimation of LAIPCA was within 12%. Woody area index (WAI) was evaluated with the PCA during the leafless period. The instrument was able to show the difference between thinned (0.55±0.09) and unthinned stands (0.80±0.19). Similar to other studies, the subtraction of WAI from LAIPCA values increased the underestimation of LAILT. The agreement between the two methods for LAI estimation was satisfactory. Nevertheless, the underestimation by the PCA method must be taken into account. Over different years and stands, the variability of underestimation was not marked, pointing to a reliable use of PCA in different conditions, as, for example, the comparison of thinned and unthinned stands and in measuring temporal and spatial variations of LAI. The overlapping of leaves, the presence of gaps within the canopy and light at the horizon level seem to be some of the important variables that influence LAI estimation by the PCA. Further corrections of the data can improve substantially the performance of the PCA and produce reliable LAI estimates even though the collection of direct reference measurements is strongly recommended to know exactly LAI, in order to assess instrument performance in a given site.