Summary: This paper derives truncated union bounds on the a priori index crossover probabilities \(p(j|i)\) that result when an \(n\)-bit data index \(i\) is convolutionally encoded, transmitted over a noisy channel, and decoded with the Viterbi algorithm, giving received index \(j\). The bounds are derived with a modified transfer function technique, using \(n\)-stage state transition matrices with symbolic labels. The technique is easily automated with commercial symbolic algebra packages. Bounds are obtained for convolutional and trellis-coded modulation codes over binary-symmetric and additive white Gaussian noise channels. A joint source channel coding example demonstrates that the bounds on \(p(j|i)\) developed in this paper can give a \(1-3\)-dB accuracy improvement in end-to-end signal-to-noise ratio predictions when compared to predictions based on bounds on the delivered bit error probability.