PurposeTo investigate laminated object manufacturing (LOM) process quality, using a design of experiments approach.Design/methodology/approachThe quality characteristics measured were in‐plane dimensional accuracy, actual layer thickness (ALT), and mean time per layer. The process parameters tested were nominal layer thickness (LT), heater temperature (HT), platform retract (PR), heater speed (HS), laser speed (LS), feeder speed (FS) and platform speed (PS). A typical test part has been used, and matrix experiments were carried out based on Taguchi design. Optimal process parameter values were identified and finally, additive and regression models were applied to the experimental results and tested using evaluation experiments.FindingsThe statistical analysis of the experimental results shows that error in X direction was higher than error in Y direction. Dimensional accuracy in X direction depends mainly on the HS (89 percent) and HT (5 percent), and in Y direction on HS (50 percent), LT (31 percent), LS (9 percent), PS (6 percent), and HT (3 percent). On the other hand, ALT depends mainly on the nominal ALT (96 percent), HS (2 percent), HT (1 percent), and PR (1 percent). Finally, mean time per layer depends mainly on HS (59 percent), LS (17 percent), FS (17 percent), and PS (4 percent).Research limitations/implicationsFuture work should involve extensive matrix experiments using parameters such as dimensions of test part (X_max, Y_max, Z_max), hatch spacing in X and Y directions, and delay time between sequential layers.Practical implicationsUsing the extracted models, the quality of LOM parts can be predicted and appropriate process parameter values selected. This means minimization of post processing time, easier disengagement between supporting frame and part, easier decubing, process optimization, less finishing and satisfactory final LOM parts or tools. Also, ALT prediction and mean time per layer analysis could be used to improve LOM build time predictions.Originality/valueThe above analysis is useful for LOM users when predictions of part quality, paper consumption, and build time are needed. This methodology could be easily applied to different materials and initial conditions for optimisation of other LOM‐type processes.