Tractography refers to the in vivo reconstruction of fiber bundles, e.g., in brain, via the analysis of anisotropic diffusion patterns measured by diffusion weighted magnetic resonance imaging (DWI). The data provides a probabilistic model of local diffusion which was shown to correlate with the underlying fibrous structure under certain assumptions. Deterministic tractography suffers from uncertainties at kissing and crossing fibers, at different levels depending on the diffusion model employed (e.g., DTI, HARDI), yet it is easy to interpret and use in clinic. In this study, a novel generic algorithm, split and merge tractography (SMT), is proposed that provides a real-time, interactive and reliability ranked assessment of potential pathways, communicating the true information content of the data without sacrificing the usability of tractography. Specifically, SMT takes in a precomputed set of tracts and the diffusion data (e.g., DTI, HARDI) as its input, generates a set of short (reliable) tracts via splitting at unreliable points and forms quasi-random clusters of short tracts by means of which the space of short tract clusters, representing complete tracts, is sampled. A histogram of thus formed clusters is built in an efficient way and used for real-time, interactive assessment of pathways. The current implementation uses DTI and fourth-order Runge-Kutta integration based streamline tractography as its input. The method is qualitatively assessed on phantom DTI data and real DTI data. Phantom experiments demonstrated that SMT is capable of highlighting the problematic regions and suggesting pathways that are completely overseen by input streamline tractography. Real data experiment results correlate well with known anatomy and also demonstrate that the reliability ranking can efficiently suppress the erroneous tracts interactively. The method is compared to a recent method that also pursues a similar approach, yet in a global optimization based framework. The c- mparative study on real DTI data revealed the lower computational load of SMT and a better correlation with known anatomy.