We present the development and validation of a new multivariate b jet identification algorithm (“b tagger”) used at the CDF experiment at the Fermilab Tevatron. At collider experiments, b taggers allow one to distinguish particle jets containing B hadrons from other jets. Employing feedforward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/e ciency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the e ectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag e ciency as a function of the placement of a lower threshold on a jet’s neural network output value in Z + 1 jet and t¯ t candidate samples, rich in light flavor and b jets, respectively.