Extraction of demographic, cultural background characteristics or psychometric traits about an author from an anonymous text has a number of potential applications in such fields as forensics, security or user-targeted services. Despite significant advances in the automatic author profiling, the most of the research has been done on Germanic languages and not so much on morphologically rich languages. Consequently, this work is the first attempt at finding a good method for solving automatic author profiling in three dimensions for Lithuanian: age (6 categories), gender (2 categories) and political view (3 categories). To tackle this task we used the dataset, which contains text transcripts of Lithuanian parliamentary speeches and debates, thus representing formal spoken, but normative Lithuanian language. In our paper we explored different feature types (ultimate style markers, lexical, morphological, character, and aggregated) and dataset sizes (of 100, 200, 500, 1,000, 2,000, 5,000 instances in each category). The best results were obtained with Support Vector Machine method, the largest tested dataset and lemmas as features: i.e. 44.6% of accuracy for age with interpolation up to trigrams, 74.6% for gender and 58.7% for political view with interpolation up to bigrams.