Manual interpretation of large volumes of seismic data is a tedious and time-consuming process. Seismic interpretation relies on extensive expert knowledge of geological rules, as well as strong geophysical interpretation skills. Moreover, in modern approaches to reservoir characterization, a single, deterministic reservoir model interpreted from seismic information is commonly of less interest than the multiple potential interpretations that can arise from the data. Hence, an understanding of the uncertainty associated with seismic interpretation and its quantification are extremely important to reservoir management and decision analysis. In this chapter, several new tools are presented, most of them based on statistical pattern recognition, that can aid the interpreter in constructing a seismic-based reservoir model and provide some uncertainty quantification. Two groups of methods based on unsupervised and supervised pattern detection are discussed. A new geostatistical approach termed feature-based geostatistics is introduced, the aim of which is to accurately reproduce facies shapes. All methods are validated using an interpreted seismic data set representing channel facies from a turbidite sequence in Gabon, west Africa.