The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises genuine, simulated signatures produced by unskilled imitators or random signatures (genuine signatures from other writers). Ten systems were evaluated, and some interesting results are presented in terms of accuracy and execution time. The top ranking system attained an overall error of 8.94%. This result interestingly correlates with the top ranking accuracy achieved in a previous signature verification competition at ICDAR 2009.