Over the last few years, research in Artificial Intelligence has focussed on games with incomplete information and non-deterministic moves. The game of Poker is a perfect theme for studying this subject. The best known Poker variant is Texas Hold'em that combines simple rules with a huge amount of possible playing strategies. This paper is focussed on developing algorithms for performing simple online opponent modelling in Texas Hold'em Poker enabling to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behaviour and an observer agent was developed, capable of using simple opponent modelling techniques, in order to select the best playing strategy against each opponent. The results obtained in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent is clearly capable of outperforming all their counterparts in all tests performed.